Communication-Efficient Federated Learning under Non-IID Data: Aggregation and Compression Strategies

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As data in scenarios such as medical, transportation and the Internet of Things continue to be dispersed to institutions and edge devices, how to balance the quality of model training and communication costs without concentrating raw data has become an important issue in the deployment of federated learning. Federated learning faces a coupling bottleneck between client drift and communication overhead under non-independent and non-identically distributed (Non-IID) data. This paper reviews aggregation, compression and hybrid strategies with representative studies, and verifies Federated Averaging (FedAvg), Federated Proximal (FedProx) and their error-feedback Top-k combinations (FedAvg+EF-Top-k and FedProx+EF-Top-k) under the unified setting of Fashion-MNIST. The results show that compression can significantly reduce the cumulative uplink communication required to reach the target accuracy and enable more training rounds under a fixed total communication budget. However, strong heterogeneity is associated with greater run-to-run variability, and communication rounds, transmitted bits, and end-to-end time cannot be substituted for one another. Based on literature comparison and verification results, this paper proposes a selection strategy according to data heterogeneity, bandwidth and system constraints, and recommends the unified reporting of accuracy, communication volume, communication rounds, time, and energy consumption.