HybridCom: Improve Federated Learning Efficiency on Unstable Data

Federated learning (FL) has made significant advancements in recent years. However, its efficiency on unstable distributed data remains a critical challenge. This stems from oversights in existing FL frameworks regarding the instability of global and client private data distributions or their assumption of stable distributions over time. To address this challenge, we present HybridCom, an efficient FL framework on unstable data. The core concept of HybridCom is to adjust the client participation probability based on their contributions to adapting the global model to data distribution changes. This is achieved through a hybrid contribution indicator that includes a performance-based client-side indicator and a gradient-based server-side indicator. Based on the results of the contribution indicator, HybridCom integrates a probabilistic participation controller to dynamically adjust the participation probability of each client during the FL process. By utilizing Hybrid-Com, clients undergoing data distribution changes that are not perceived by the global model have a higher probability of participating. This makes HybridCom more efficient in adapting to unstable data distributions. The experimental results demonstrate that HybridCom surpasses the baseline models, achieving an approximate 1.3% improvement across diverse simulation settings and communication resource constraints.