Polar Codes Enhance Federated Learning Communication Efficiency

Researchers have introduced a novel federated learning (FL) scheme that integrates polar codes to address critical communication challenges. FL, which enables collaborative model training across distributed devices without raw data sharing, often faces significant bottlenecks and channel impairments. Traditional approaches either assume error-free channels or apply uniform error protection, failing to account for the varying importance of quantization bits within model updates. The proposed cross-layer design leverages the unequal error protection (UEP) property of polar codes, particularly under finite block lengths. It strategically protects the more significant quantization bits of transmitted model updates, thereby enhancing resilience against channel noise. The study includes a rigorous convergence analysis, providing an upper bound on the convergence gap, which is then optimized by jointly considering the number of quantization bits and polar code block length. Experimental results confirm that this polar code-based scheme consistently outperforms uncoded and LDPC-based equal error protection benchmarks, especially as channel quality degrades, proving its efficacy in improving FL robustness and efficiency.