Enhancing Human Activity Recognition With FedPA: Focusing on Non-IID Data Challenges in Federated Learning

Federated learning (FL) revolutionizes distributed learning in human activity recognition (HAR) by allowing clients to train models locally and share only model parameters, thus optimizing data usage and mitigating privacy concerns. However, the presence of nonindependent and identically distributed (Non-IID) data across clients impedes FL efficiency between the server and clients, affecting HAR performance and communication efficiency. Acknowledging the significance of posttraining parameters as intrinsic representations of knowledge acquired by client models, we introduce federated parameters averaging (FedPA), a novel algorithm for model aggregation. This algorithm strategically assigns varying weight coefficients to clients during aggregation, accurately reflecting each client’s learning contribution and thereby enhancing the efficacy of both global and client models. We conducted empirical analyses using real-world datasets to assess FedPA’s effectiveness. The results demonstrate that FedPA not only preserves accuracy but also improves communication efficiency compared to existing FL aggregation algorithms, like FedAvg, FedMA, and FedCDA. These findings underscore FedPA’s superiority in addressing Non-IID challenges in HAR tasks and highlight its potential to improve overall model performance in FL settings.