Machine learning (ML) typically relies on the assumption that training and testing distributions are identical and that data are centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly, and data are often distributed across different devices, organizations, or edge nodes. Consequently, it is to develop models capable of effectively generalizing across unseen distributions in data spanning various domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG synergizes federated learning (FL) and domain generalization (DG) techniques, facilitating collaborative model development across diverse source domains for effective generalization to unseen domains, all while maintaining data privacy. However, generalizing the federated model under domain shifts remains a complex, underexplored issue. This article provides a comprehensive survey of the latest advancements in this field. Initially, we discuss the development process from traditional ML to domain adaptation (DA) and DG, leading to FDG, as well as provide the corresponding formal definition. Subsequently, we classify recent methodologies into four distinct categories: federated domain alignment (FDAL), data manipulation (DM), learning strategies (LSs), and aggregation optimization (AO), detailing appropriate algorithms for each. We then overview commonly utilized datasets, applications, evaluations, and benchmarks. Conclusively, this survey outlines potential future research directions.
{"title":"Federated Domain Generalization: A Survey","authors":"Ying Li;Xingwei Wang;Rongfei Zeng;Praveen Kumar Donta;Ilir Murturi;Min Huang;Schahram Dustdar","doi":"10.1109/JPROC.2025.3596173","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3596173","url":null,"abstract":"Machine learning (ML) typically relies on the assumption that training and testing distributions are identical and that data are centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly, and data are often distributed across different devices, organizations, or edge nodes. Consequently, it is to develop models capable of effectively generalizing across unseen distributions in data spanning various domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG synergizes federated learning (FL) and domain generalization (DG) techniques, facilitating collaborative model development across diverse source domains for effective generalization to unseen domains, all while maintaining data privacy. However, generalizing the federated model under domain shifts remains a complex, underexplored issue. This article provides a comprehensive survey of the latest advancements in this field. Initially, we discuss the development process from traditional ML to domain adaptation (DA) and DG, leading to FDG, as well as provide the corresponding formal definition. Subsequently, we classify recent methodologies into four distinct categories: federated domain alignment (FDAL), data manipulation (DM), learning strategies (LSs), and aggregation optimization (AO), detailing appropriate algorithms for each. We then overview commonly utilized datasets, applications, evaluations, and benchmarks. Conclusively, this survey outlines potential future research directions.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 4","pages":"370-410"},"PeriodicalIF":25.9,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-13DOI: 10.1109/JPROC.2025.3593952
Andreas Triantafyllopoulos;Iosif Tsangko;Alexander Gebhard;Annamaria Mesaros;Tuomas Virtanen;Björn W. Schuller
Foundation models (FMs) are increasingly spearheading recent advances on a variety of tasks that fall under the purview of computer audition—i.e., the use of machines to understand sounds. They feature several advantages over traditional pipelines: among others, the ability to consolidate multiple tasks in a single model, the option to leverage knowledge from other modalities, and the readily available interaction with human users. Naturally, these promises have created substantial excitement in the audio community and have led to a wave of early attempts to build new, generalpurpose FMs for audio. In the present contribution, we give an overview of computational audio analysis as it transitions from traditional pipelines toward auditory FMs. Our work highlights the key operating principles that underpin those models and showcases how they can accommodate multiple tasks that the audio community previously tackled separately.
{"title":"Computer Audition: From Task-Specific Machine Learning to Foundation Models","authors":"Andreas Triantafyllopoulos;Iosif Tsangko;Alexander Gebhard;Annamaria Mesaros;Tuomas Virtanen;Björn W. Schuller","doi":"10.1109/JPROC.2025.3593952","DOIUrl":"10.1109/JPROC.2025.3593952","url":null,"abstract":"Foundation models (FMs) are increasingly spearheading recent advances on a variety of tasks that fall under the purview of computer audition—i.e., the use of machines to understand sounds. They feature several advantages over traditional pipelines: among others, the ability to consolidate multiple tasks in a single model, the option to leverage knowledge from other modalities, and the readily available interaction with human users. Naturally, these promises have created substantial excitement in the audio community and have led to a wave of early attempts to build new, generalpurpose FMs for audio. In the present contribution, we give an overview of computational audio analysis as it transitions from traditional pipelines toward auditory FMs. Our work highlights the key operating principles that underpin those models and showcases how they can accommodate multiple tasks that the audio community previously tackled separately.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 4","pages":"317-343"},"PeriodicalIF":25.9,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11124350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-30DOI: 10.1109/jproc.2025.3582502
William J. Blackwell, Scott A. Braun, George R. Alvey, Robert Atlas, Ralf Bennartz, Jessica Braun, Kerri Cahoy, Ruiyao Chen, Galina Chirokova, Brittany Dahl, James Darlow, Mark DeMaria, Michael Diliberto, Jason P. Dunion, Patrick Duran, Thomas J. Greenwald, Sarah Griffin, Zachary Griffith, Derrick Herndon, Jeffrey D. Hawkins, Satya Kalluri, C. Kidd, Min-Jeong Kim, R. Vincent Leslie, Frank Marks, Toshi Matsui, W. McCarty, Adam Milstein, Glenn Perras, Michael L. Pieper, Robert Rogers, Christopher Velden, Yalei You, Nick V. Zorn
{"title":"High Revisit-Rate Tropical Cyclone Observations From the NASA TROPICS Satellite Constellation Mission","authors":"William J. Blackwell, Scott A. Braun, George R. Alvey, Robert Atlas, Ralf Bennartz, Jessica Braun, Kerri Cahoy, Ruiyao Chen, Galina Chirokova, Brittany Dahl, James Darlow, Mark DeMaria, Michael Diliberto, Jason P. Dunion, Patrick Duran, Thomas J. Greenwald, Sarah Griffin, Zachary Griffith, Derrick Herndon, Jeffrey D. Hawkins, Satya Kalluri, C. Kidd, Min-Jeong Kim, R. Vincent Leslie, Frank Marks, Toshi Matsui, W. McCarty, Adam Milstein, Glenn Perras, Michael L. Pieper, Robert Rogers, Christopher Velden, Yalei You, Nick V. Zorn","doi":"10.1109/jproc.2025.3582502","DOIUrl":"https://doi.org/10.1109/jproc.2025.3582502","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"26 1","pages":""},"PeriodicalIF":20.6,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144747636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-28DOI: 10.1109/JPROC.2025.3587420
{"title":"Future Special Issues/Special Sections of the Proceedings","authors":"","doi":"10.1109/JPROC.2025.3587420","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3587420","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"312-312"},"PeriodicalIF":23.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-28DOI: 10.1109/JPROC.2025.3587416
{"title":"Proceedings of the IEEE Publication Information","authors":"","doi":"10.1109/JPROC.2025.3587416","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3587416","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"C2-C2"},"PeriodicalIF":23.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098583","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-28DOI: 10.1109/JPROC.2025.3583866
Summary form only: Abstracts of articles presented in this issue of the publication.
仅以摘要形式提供：本刊发表的文章摘要。
{"title":"Scanning the Issue","authors":"","doi":"10.1109/JPROC.2025.3583866","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3583866","url":null,"abstract":"Summary form only: Abstracts of articles presented in this issue of the publication.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"210-212"},"PeriodicalIF":23.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098578","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-28DOI: 10.1109/JPROC.2025.3587422
{"title":"IEEE Membership","authors":"","doi":"10.1109/JPROC.2025.3587422","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3587422","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"C3-C3"},"PeriodicalIF":23.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-28DOI: 10.1109/JPROC.2025.3587424
{"title":"Proceedings of the IEEE: Stay Informed. Become Inspired.","authors":"","doi":"10.1109/JPROC.2025.3587424","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3587424","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"C4-C4"},"PeriodicalIF":23.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098580","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1109/JPROC.2025.3580189
{"title":"IEEE Membership","authors":"","doi":"10.1109/JPROC.2025.3580189","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3580189","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 2","pages":"C3-C3"},"PeriodicalIF":23.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082634","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1109/JPROC.2025.3580183
{"title":"Proceedings of the IEEE Publication Information","authors":"","doi":"10.1109/JPROC.2025.3580183","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3580183","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 2","pages":"C2-C2"},"PeriodicalIF":23.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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