Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31275
Peter Lindes, Steven Jones
Humans acquire language and related concepts in a trajectory over a lifetime. Concepts for simple interaction with the world are learned before language. Later, words are learned to name these concepts along with structures needed to represent larger meanings. Eventually, language advances to where it can drive the learning of new concepts. Throughout this trajectory a language processing capability uses architectural mechanisms to process language using the knowledge already acquired. We assume that this growing body of knowledge is made up of small units of form-meaning mapping that can be composed in many ways, suggesting that these units are learned incrementally from experience. In prior work we have built a system to comprehend human language within an autonomous robot using knowledge in such units developed by hand. Here we propose a research program to develop the ability of an artificial agent to acquire this knowledge incrementally and autonomously from its experience in a similar trajectory. We then propose a strategy for evaluating this human-like learning system using a large benchmark created as a tool for training deep learning systems. We expect that our human-like learning system will produce better task performance from training on only a small subset of this benchmark.
{"title":"Modeling Human-Like Acquisition of Language and Concepts","authors":"Peter Lindes, Steven Jones","doi":"10.1609/aaaiss.v3i1.31275","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31275","url":null,"abstract":"Humans acquire language and related concepts in a trajectory over a lifetime. Concepts for simple interaction with the world are learned before language. Later, words are learned to name these concepts along with structures needed to represent larger meanings. Eventually, language advances to where it can drive the learning of new concepts. Throughout this trajectory a language processing capability uses architectural mechanisms to process language using the knowledge already acquired. We assume that this growing body of knowledge is made up of small units of form-meaning mapping that can be composed in many ways, suggesting that these units are learned incrementally from experience. In prior work we have built a system to comprehend human language within an autonomous robot using knowledge in such units developed by hand. Here we propose a research program to develop the ability of an artificial agent to acquire this knowledge incrementally and autonomously from its experience in a similar trajectory. We then propose a strategy for evaluating this human-like learning system using a large benchmark created as a tool for training deep learning systems. We expect that our human-like learning system will produce better task performance from training on only a small subset of this benchmark.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"81 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31238
Yuto Nakashima
Generating preferred images from GANs is a challenging task due to the high-dimensional nature of latent space. In this study, we propose a novel approach that uses simple user-swipe interactions to generate preferred images from users. To effectively explore the latent space with only swipe interactions, we apply principal component analysis to the latent space of StyleGAN, creating meaningful subspaces. Additionally, we use a multi-armed bandit algorithm to decide which dimensions to explore, focusing on the user's preferences. Our experiments show that our method is more efficient in generating preferred images than the baseline.
由于潜在空间的高维特性，从 GAN 生成首选图像是一项具有挑战性的任务。在本研究中，我们提出了一种新方法，利用简单的用户滑动交互从用户生成首选图片。为了有效地利用刷卡交互探索潜在空间，我们对 StyleGAN 的潜在空间进行了主成分分析，从而创建了有意义的子空间。此外，我们还使用多臂匪徒算法来决定探索哪些维度，重点关注用户的偏好。实验表明，我们的方法在生成首选图片方面比基线方法更有效。
{"title":"Personalized Image Generation Through Swiping","authors":"Yuto Nakashima","doi":"10.1609/aaaiss.v3i1.31238","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31238","url":null,"abstract":"Generating preferred images from GANs is a challenging task due to the high-dimensional nature of latent space. In this study, we propose a novel approach that uses simple user-swipe interactions to generate preferred images from users. To effectively explore the latent space with only swipe interactions, we apply principal component analysis to the latent space of StyleGAN, creating meaningful subspaces. Additionally, we use a multi-armed bandit algorithm to decide which dimensions to explore, focusing on the user's preferences. Our experiments show that our method is more efficient in generating preferred images than the baseline.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"7 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31200
Orfeas Menis Mastromichalakis, Edmund Dervakos, A. Chortaras, G. Stamou
The use of symbolic knowledge representation and reasoning as a way to resolve the lack of transparency of machine learning classifiers is a research area that has lately gained a lot of traction. In this work, we use knowledge graphs as the underlying framework providing the terminology for representing explanations for the operation of a machine learning classifier escaping the constraints of using the features of raw data as a means to express the explanations, providing a promising solution to the problem of the understandability of explanations. In particular, given a description of the application domain of the classifier in the form of a knowledge graph, we introduce a novel theoretical framework for representing explanations of its operation, in the form of query-based rules expressed in the terminology of the knowledge graph. This allows for explaining opaque black-box classifiers, using terminology and information that is independent of the features of the classifier and its domain of application, leading to more understandable explanations but also allowing the creation of different levels of explanations according to the final end-user.
{"title":"Rule-Based Explanations of Machine Learning Classifiers Using Knowledge Graphs","authors":"Orfeas Menis Mastromichalakis, Edmund Dervakos, A. Chortaras, G. Stamou","doi":"10.1609/aaaiss.v3i1.31200","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31200","url":null,"abstract":"The use of symbolic knowledge representation and reasoning as a way to resolve the lack of transparency of machine learning classifiers is a research area that has lately gained a lot of traction. In this work, we use knowledge graphs as the underlying framework providing the terminology for representing explanations for the operation of a machine learning classifier escaping the constraints of using the features of raw data as a means to express the explanations, providing a promising solution to the problem of the understandability of explanations. In particular, given a description of the application domain of the classifier in the form of a knowledge graph, we introduce a novel theoretical framework for representing explanations of its operation, in the form of query-based rules expressed in the terminology of the knowledge graph. This allows for explaining opaque black-box classifiers, using terminology and information that is independent of the features of the classifier and its domain of application, leading to more understandable explanations but also allowing the creation of different levels of explanations according to the final end-user.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"22 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31209
N. Tan, Niket Tandon, David Wadden, Oyvind Tafjord, M. Gahegan, Michael Witbrock
Claim verification in scientific domains requires models that faithfully incorporate relevant knowledge from the ever-growing, vast existing literature. Unfaithful claim verifications can lead to misinformation such as those observed during the COVID-19 pandemic. Fact-checking systems often fail to capture the complex relationship between claims and evidence, especially with ambiguous claims and implicit assumptions. Relying only on current LLMs poses challenges due to hallucinations and information traceability issues. To address these challenges, our approach considers multiple viewpoints onto the scientific literature, enabling the assessment of contradictory arguments and implicit assumptions. Our proposed inference method adds faithful reasoning to large language models by distilling information from diverse, relevant scientific abstracts. This method provides a verdict label that can be weighted by the reputation of the scientific articles and an explanation that can be traced back to sources. Our findings demonstrate that humans not only perceive our explanation to be significantly superior to the off-the-shelf model, but they also evaluate it as faithfully enabling the tracing of evidence back to its original sources.
{"title":"Faithful Reasoning over Scientific Claims","authors":"N. Tan, Niket Tandon, David Wadden, Oyvind Tafjord, M. Gahegan, Michael Witbrock","doi":"10.1609/aaaiss.v3i1.31209","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31209","url":null,"abstract":"Claim verification in scientific domains requires models that faithfully incorporate relevant knowledge from the ever-growing, vast existing literature. \u0000Unfaithful claim verifications can lead to misinformation such as those observed during the COVID-19 pandemic. Fact-checking systems often fail to capture the complex relationship between claims and evidence, especially with ambiguous claims and implicit assumptions. Relying only on current LLMs poses challenges due to hallucinations and information traceability issues. To address these challenges, our approach considers multiple viewpoints onto the scientific literature, enabling the assessment of contradictory arguments and implicit assumptions. Our proposed inference method adds faithful reasoning to large language models by distilling information from diverse, relevant scientific abstracts. This method provides a verdict label that can be weighted by the reputation of the scientific articles and an explanation that can be traced back to sources. Our findings demonstrate that humans not only perceive our explanation to be significantly superior to the off-the-shelf model, but they also evaluate it as faithfully enabling the tracing of evidence back to its original sources.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"77 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31270
Brendan Conway-Smith, Robert L. West
Large Language Models (LLMs) lack robust metacognitive learning abilities and depend on human-provided algorithms and prompts for learning and output generation. Metacognition involves processes that monitor and enhance cognition. Learning how to learn - metacognitive learning - is crucial for adapting and optimizing learning strategies over time. Although LLMs possess limited metacognitive abilities, they cannot autonomously refine or optimize these strategies. Humans possess innate mechanisms for metacognitive learning that enable at least two unique abilities: discerning which metacognitive strategies are best and automatizing learning strategies. These processes have been effectively modeled in the ACT-R cognitive architecture, providing insights on a path toward greater learning autonomy in AI. Incorporating human-like metacognitive learning abilities into AI could potentially lead to the development of more autonomous and versatile learning mechanisms, as well as improved problem-solving capabilities and performance across diverse tasks.
{"title":"Toward Autonomy: Metacognitive Learning for Enhanced AI Performance","authors":"Brendan Conway-Smith, Robert L. West","doi":"10.1609/aaaiss.v3i1.31270","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31270","url":null,"abstract":"Large Language Models (LLMs) lack robust metacognitive learning abilities and depend on human-provided algorithms and prompts for learning and output generation. Metacognition involves processes that monitor and enhance cognition. Learning how to learn - metacognitive learning - is crucial for adapting and optimizing learning strategies over time. Although LLMs possess limited metacognitive abilities, they cannot autonomously refine or optimize these strategies. Humans possess innate mechanisms for metacognitive learning that enable at least two unique abilities: discerning which metacognitive strategies are best and automatizing learning strategies. These processes have been effectively modeled in the ACT-R cognitive architecture, providing insights on a path toward greater learning autonomy in AI. Incorporating human-like metacognitive learning abilities into AI could potentially lead to the development of more autonomous and versatile learning mechanisms, as well as improved problem-solving capabilities and performance across diverse tasks.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"35 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31274
Steven Jones, Peter Lindes
Human-like learning includes an ability to learn concepts from a stream of embodiment sensor data. Echoing previous thoughts such as those from Barsalou that cognition and perception share a common representation system, we suggest an addendum to the common model of cognition. This addendum poses a simultaneous semantic memory and perception learning that bypasses working memory, and that uses parallel processing to learn concepts apart from deliberate reasoning. The goal is to provide a general outline for how to extend a class of cognitive architectures to implement a more human-like interface between cognition and embodiment of an agent, where a critical aspect of that interface is that it is dynamic because of learning.
{"title":"Toward Human-Like Representation Learning for Cognitive Architectures","authors":"Steven Jones, Peter Lindes","doi":"10.1609/aaaiss.v3i1.31274","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31274","url":null,"abstract":"Human-like learning includes an ability to learn concepts from a stream of embodiment sensor data. Echoing previous thoughts such as those from Barsalou that cognition and perception share a common representation system, we suggest an addendum to the common model of cognition. This addendum poses a simultaneous semantic memory and perception learning that bypasses working memory, and that uses parallel processing to learn concepts apart from deliberate reasoning. The goal is to provide a general outline for how to extend a class of cognitive architectures to implement a more human-like interface between cognition and embodiment of an agent, where a critical aspect of that interface is that it is dynamic because of learning.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"21 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31178
Shih-Hong Huang, Ting-Hao 'Kenneth' Huang
It is easy to assume that Large Language Models (LLMs) will seamlessly take over applications, especially those that are largely automated. In the case of conversational voice assistants, commercial systems have been widely deployed and used over the past decade. However, are we indeed on the cusp of the future we envisioned? There exists a social-technical gap between what people want to accomplish and the actual capability of technology. In this paper, we present a case study comparing two voice assistants built on Amazon Alexa: one employing a human-in-the-loop workflow, the other utilizes LLM to engage in conversations with users. In our comparison, we discovered that the issues arising in current human-in-the-loop and LLM systems are not identical. However, the presence of a set of similar issues in both systems leads us to believe that focusing on the interaction between users and systems is crucial, perhaps even more so than focusing solely on the underlying technology itself. Merely enhancing the performance of the workers or the models may not adequately address these issues. This observation prompts our research question: What are the overlooked contributing factors in the effort to improve the capabilities of voice assistants, which might not have been emphasized in prior research?
{"title":"On Replacing Humans with Large Language Models in Voice-Based Human-in-the-Loop Systems","authors":"Shih-Hong Huang, Ting-Hao 'Kenneth' Huang","doi":"10.1609/aaaiss.v3i1.31178","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31178","url":null,"abstract":"It is easy to assume that Large Language Models (LLMs) will seamlessly take over applications, especially those that are largely automated. In the case of conversational voice assistants, commercial systems have been widely deployed and used over the past decade. However, are we indeed on the cusp of the future we envisioned? There exists a social-technical gap between what people want to accomplish and the actual capability of technology. In this paper, we present a case study comparing two voice assistants built on Amazon Alexa: one employing a human-in-the-loop workflow, the other utilizes LLM to engage in conversations with users. In our comparison, we discovered that the issues arising in current human-in-the-loop and LLM systems are not identical. However, the presence of a set of similar issues in both systems leads us to believe that focusing on the interaction between users and systems is crucial, perhaps even more so than focusing solely on the underlying technology itself. Merely enhancing the performance of the workers or the models may not adequately address these issues. This observation prompts our research question: What are the overlooked contributing factors in the effort to improve the capabilities of voice assistants, which might not have been emphasized in prior research?","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"45 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141118958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31284
Irina Rabkina
Neurotypical adult humans are impeccably good social reasoners. Despite the occasional faux pas, we know how to interact in most social settings and how to consider others' points of view. Young children, on the other hand, do not. Social reasoning, like many of our most important skills, is learned. Much like human children, AI agents are not good social reasoners. While some algorithms can perform some aspects of social reasoning, we are a ways off from AI that can interact naturally and appropriately in the broad range of settings that people can. In this talk, I will argue that learning social reasoning via the same processes used by people will help AI agents reason--and interact--more like people do. Specifically, I will argue that children learn social reasoning via analogy, and that AI agents should, too. I will present evidence from cognitive modeling experiments demonstrating the former and AI experiments demonstrating the latter. I will also propose future directions for social reasoning research that both demonstrate the need for robust, human-like social reasoning in AI and test the utility of common approaches.
{"title":"Human-Like Learning of Social Reasoning via Analogy","authors":"Irina Rabkina","doi":"10.1609/aaaiss.v3i1.31284","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31284","url":null,"abstract":"Neurotypical adult humans are impeccably good social reasoners. Despite the occasional faux pas, we know how to interact in most social settings and how to consider others' points of view. Young children, on the other hand, do not. Social reasoning, like many of our most important skills, is learned. \u0000\u0000Much like human children, AI agents are not good social reasoners. While some algorithms can perform some aspects of social reasoning, we are a ways off from AI that can interact naturally and appropriately in the broad range of settings that people can. In this talk, I will argue that learning social reasoning via the same processes used by people will help AI agents reason--and interact--more like people do. Specifically, I will argue that children learn social reasoning via analogy, and that AI agents should, too. I will present evidence from cognitive modeling experiments demonstrating the former and AI experiments demonstrating the latter. I will also propose future directions for social reasoning research that both demonstrate the need for robust, human-like social reasoning in AI and test the utility of common approaches.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"10 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31266
Christelle Scharff, James Brusseau, K. Bathula, Kaleemunnisa Fnu, Samyak Rakesh Meshram, Om Gaikhe
This paper addresses the ethics of inclusion in artificial in-telligence in the context of African fashion. Despite the proliferation of fashion-related AI applications and da-tasets global diversity remains limited, and African fash-ion is significantly underrepresented. This paper docu-ments two use-cases that enhance AI's inclusivity by in-corporating sub-Saharan fashion elements. The first case details the creation of a Senegalese fashion dataset and a model for classifying traditional apparel using transfer learning. The second case investigates African wax textile patterns generated through generative adversarial net-works (GANs), specifically StyleGAN architectures, and machine learning diffusion models. Alongside the practi-cal, technological advances, theoretical ethical progress is made in two directions. First, the cases are used to elabo-rate and define the ethics of inclusion, while also contrib-uting to current debates about how inclusion differs from ethical fairness. Second, the cases engage with the ethical debate on whether AI innovation should be slowed to prevent ethical imbalances or accelerated to solve them.
{"title":"Inclusion Ethics in AI: Use Cases in African Fashion","authors":"Christelle Scharff, James Brusseau, K. Bathula, Kaleemunnisa Fnu, Samyak Rakesh Meshram, Om Gaikhe","doi":"10.1609/aaaiss.v3i1.31266","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31266","url":null,"abstract":"This paper addresses the ethics of inclusion in artificial in-telligence in the context of African fashion. Despite the proliferation of fashion-related AI applications and da-tasets global diversity remains limited, and African fash-ion is significantly underrepresented. This paper docu-ments two use-cases that enhance AI's inclusivity by in-corporating sub-Saharan fashion elements. The first case details the creation of a Senegalese fashion dataset and a model for classifying traditional apparel using transfer learning. The second case investigates African wax textile patterns generated through generative adversarial net-works (GANs), specifically StyleGAN architectures, and machine learning diffusion models. Alongside the practi-cal, technological advances, theoretical ethical progress is made in two directions. First, the cases are used to elabo-rate and define the ethics of inclusion, while also contrib-uting to current debates about how inclusion differs from ethical fairness. Second, the cases engage with the ethical debate on whether AI innovation should be slowed to prevent ethical imbalances or accelerated to solve them.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"18 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31227
Mehreen Tahir, Muhammad Intizar Ali
Federated Learning (FL) has received tremendous attention as a decentralized machine learning (ML) framework that allows distributed data owners to collaboratively train a global model without sharing raw data. Since FL trains the model directly on edge devices, the heterogeneity of participating clients in terms of data distribution, hardware capabilities and network connectivity can significantly impact the overall performance of FL systems. Optimizing for model accuracy could extend the training time due to the diverse and resource-constrained nature of edge devices while minimizing training time could compromise the model's accuracy. Effective client selection thus becomes crucial to ensure that the training process is not only efficient but also capitalizes on the diverse data and computational capabilities of different devices. To this end, we propose FedPROM, a novel framework that tackles client selection in FL as a multi-criteria optimization problem. By leveraging the PROMETHEE method, FedPROM ranks clients based on their suitability for a given FL task, considering multiple criteria such as system resources, network conditions, and data quality. This approach allows FedPROM to dynamically select the most appropriate set of clients for each learning round, optimizing both model accuracy and training efficiency. Our evaluations on diverse datasets demonstrate that FedPROM outperforms several state-of-the-art FL client selection protocols in terms of convergence speed, and accuracy, highlighting the framework's effectiveness and the importance of multi-criteria client selection in FL.
{"title":"Multi-Criterion Client Selection for Efficient Federated Learning","authors":"Mehreen Tahir, Muhammad Intizar Ali","doi":"10.1609/aaaiss.v3i1.31227","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31227","url":null,"abstract":"Federated Learning (FL) has received tremendous attention as a decentralized machine learning (ML) framework that allows distributed data owners to collaboratively train a global model without sharing raw data. Since FL trains the model directly on edge devices, the heterogeneity of participating clients in terms of data distribution, hardware capabilities and network connectivity can significantly impact the overall performance of FL systems. Optimizing for model accuracy could extend the training time due to the diverse and resource-constrained nature of edge devices while minimizing training time could compromise the model's accuracy. Effective client selection thus becomes crucial to ensure that the training process is not only efficient but also capitalizes on the diverse data and computational capabilities of different devices. To this end, we propose FedPROM, a novel framework that tackles client selection in FL as a multi-criteria optimization problem. By leveraging the PROMETHEE method, FedPROM ranks clients based on their suitability for a given FL task, considering multiple criteria such as system resources, network conditions, and data quality. This approach allows FedPROM to dynamically select the most appropriate set of clients for each learning round, optimizing both model accuracy and training efficiency. Our evaluations on diverse datasets demonstrate that FedPROM outperforms several state-of-the-art FL client selection protocols in terms of convergence speed, and accuracy, highlighting the framework's effectiveness and the importance of multi-criteria client selection in FL.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"30 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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