Abstract
Federated learning (FL) is a decentralized approach that aims to establish a global model by aggregating updates from diverse clients without sharing their local data. However, the approach becomes complicated when Byzantine clients join with arbitrary manipulation, referred to as malicious clients. Classical techniques, such as Federated Averaging (FedAvg), are insufficient to incentivize reliable clients and discourage malicious clients. Other existing Byzantine FL schemes to address malicious clients are either incentive-reliable clients or need-to-provide server-labeled data as the public validation dataset, which increase time complexity. This study introduces a federated learning framework with an evaluator-based incentive mechanism (FedEach) that offers robustness with no dependency on server-labeled data. In this framework, we introduce evaluators and participants. Unlike the existing approaches, the server selects the evaluators and participants among the clients using model-based performance evaluation criteria such as test score and reputation. Afterward, the evaluators assess and evaluate whether a participant is reliable or malicious. Subsequently, the server exclusively aggregates models from these identified reliable participants and the evaluators for global model updates. After this aggregation, the server calculates each client's contribution, prioritizing each client's contribution to ensure the fair recognition of high-quality updates and penalizing malicious clients based on their contributions. Empirical evidence obtained from the performance in human activity recognition (HAR) datasets highlights FedEach's effectiveness, especially in environments with a high presence of malicious clients. In addition, FedEach maintains computational efficiency so that it is reliable for efficient FL applications such as sensor-based HAR with wearable devices and mobile sensing.