SelfFed: Self-adaptive Federated Learning with Non-IID data on Heterogeneous Edge Devices for Bias Mitigation and Enhance Training Efficiency

Abstract

Federated learning (FL) offers a decentralized and collaborative training solution on resource-constraint Edge Devices (EDs) to improve a global model without sharing raw data. Standard Synchronous FL (SFL) approaches provide significant advantages in terms of data privacy and reduced communication overhead, however, face several challenges including Non-independent and identically distributed (Non-IID) data, the presence of unlabeled data, biased aggregation due to device heterogeneity and effective EDs selection to handle the straggler. To tackle these challenges, we propose a new Self-adaptive Federated Learning (SelfFed) strategy using a masked loss function to handle unlabeled data. This allows EDs to concentrate on labeled data, enhancing training efficiency. Additionally, we integrate a novel quality-dependent aggregation solution to mitigate bias during model updates through aggregation. This solution accurately reflects performance across Non-IID data distributions by incentivizing local EDs using a new Stackelberg game model. The model provides rewards based on their contributions to the global model, thereby keeping the EDs motivated to participate and perform well. Finally, we incorporate a deep reinforcement learning technique into the proposed SelfFed strategy for dynamic ED selection to handle straggler EDs. This technique adapts to changes in device performance and resources over iterations, fostering collaboration and sustained engagement. The performance of the SelfFed strategy is evaluated using a real-time SFL scenario (irrigation control in paddy fields) and three benchmark datasets using a serverless private cloud environment. Comparative results against state-of-the-art approaches reveal that the SelfFed significantly reduces CPU usage by 5%–6% and enhances training efficiency by 4%–8% while achieving 4%–6% higher accuracy. Further, in the real-time scenario, the SelfFed improves CPU usage by 3%–5% and enhances training efficiency by 8%–10% with 5%–7% higher accuracy.