A profile similarity-based personalized federated learning method for wearable sensor-based human activity recognition

Abstract

Wearable sensor-based human activity recognition (HAR) utilizes artificial intelligence models to analyze real-time data like accelerometer data to recognize daily human activities. While it greatly benefits the life of senior citizens and postoperative patients, it conventionally requires the collected data to be uploaded to a central server to train AI models, raising critical security and privacy concerns. Though Federated learning (FL) emerges as a viable way to cope with these problems, it is confronted by the data heterogeneity problem, where the varying activity patterns of different individuals result in non-identically distributed local data. Some FL models have been proposed to solve the data heterogeneity problem by leveraging the similarity between individuals to create a personalized global model for each individual. However, they are still limited by increased computation or unreliable relationships in the similarity computation. This study proposes a novel profile similarity-based personalized federated learning for wearable sensor-based HAR where the similarity between individuals can be reflected in their profile, such as age, gender, height, and weight. When personalizing a model for an individual, we compute the weighted sum of all clients’ local models, where the weight is determined by the similarity value computed from the profile. In this way, the local models from individuals who have higher similarity values will contribute more towards personalizing a model for a targeted individual than those who are less similar. Experiment results demonstrate that the proposed model outperformed the baseline FL and centralized learning on both RealWorld and SisFall datasets. We also discuss the tradeoff between privacy and personalization and FL's advantages over centralized learning.