Confidence-Based Similarity-Aware Personalized Federated Learning for Autonomous IoT

Federated learning facilitates collaborative model training in the autonomous IoT system while preserving the privacy of local data on IoT clients. Nonetheless, the inherent non-IID characteristic of local data leads to poor convergence of a global model. Moreover, the global model fails to satisfy the personalized task demands of all clients. To address the above issues, this paper studies client grouping and local model aggregation in federated learning from two perspectives: measure of client data distribution and distribution similarity among clients. To this end, a novel confidence-based similarity-aware personalized federated learning algorithm (FedCS) for personalized autonomous IoT is proposed by developing three key innovations, namely, a public average confidence (PAC) measure, a client grouping strategy with dynamic sampling (CGDS) and a sequential aggregated weight (SAW) strategy. Specifically, the PAC measure utilizes a public dataset on the server side to estimate the client’s data distribution, which promotes fair estimate of distribution similarity among clients while minimizing privacy risks. The CGDS strategy focuses on distribution similarity among clients and approximates the client grouping problem as an auxiliary task selection problem in multi-task learning. This strategy assigns a client into multiple groups and enables the valuable information from each client to circulate among multiple groups. The SAW strategy further incentivizes more similar clients within a group to share greater knowledge, and generates an adaptive aggregated weight for each client within a group. A thorough experiment on CIFAR10 and two healthcare benchmarks shows that FedCS achieves a superior performance.