Federated learning enhanced with polar codes for improved communication efficiency

Researchers have developed a novel federated learning (FL) scheme that utilizes polar codes to improve communication efficiency and robustness under noisy channel conditions. This cross-layer design selectively protects more significant quantization bits within model updates, a departure from conventional methods that treat all bits equally or assume error-free channels. The proposed scheme includes a rigorous convergence analysis, deriving an upper bound on the convergence gap that is optimized alongside quantization bits and polar code block length. Experimental results show substantial performance gains over uncoded and LDPC-based approaches, particularly as channel quality degrades. AI

IMPACT This research could lead to more efficient and reliable distributed AI model training, especially in environments with poor network conditions.

RANK_REASON Academic paper detailing a new method for federated learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• federated learning
• low-density parity-check code
• quantization bits

• paper
• infra

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