FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction
This addresses the problem of non-IID data for federated learning systems, offering an incremental improvement over existing methods.
The paper tackles the challenge of statistical heterogeneity in federated learning by proposing FedDC, which uses local drift variables to correct inconsistencies between local and global models, resulting in faster convergence and improved performance on image classification tasks.
Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data. However, the key challenge in federated learning is that the clients have significant statistical heterogeneity among their local data distributions, which would cause inconsistent optimized local models on the client-side. To address this fundamental dilemma, we propose a novel federated learning algorithm with local drift decoupling and correction (FedDC). Our FedDC only introduces lightweight modifications in the local training phase, in which each client utilizes an auxiliary local drift variable to track the gap between the local model parameter and the global model parameters. The key idea of FedDC is to utilize this learned local drift variable to bridge the gap, i.e., conducting consistency in parameter-level. The experiment results and analysis demonstrate that FedDC yields expediting convergence and better performance on various image classification tasks, robust in partial participation settings, non-iid data, and heterogeneous clients.