Hybrid Federated Learning for Noise-Robust Training

• URL: http://arxiv.org/abs/2601.04483v1
• Date: Thu, 08 Jan 2026 01:34:51 GMT
• Title: Hybrid Federated Learning for Noise-Robust Training
• Authors: Yongjun Kim, Hyeongjun Park, Hwanjin Kim, Junil Choi,
• Abstract summary: Federated learning (FL) and federated distillation (FD) are distributed learning paradigms that train UE models with enhanced privacy.<n>We propose a hybrid federated learning (HFL) framework in which each user equipment (UE) transmits either gradients or logits, and the base station (BS) selects the per-round weights of FL and FD updates.<n> Numerical results show that HFL achieves superior test accuracy at low SNR when both DoF are exploited.
• Score: 18.559312714944873
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) and federated distillation (FD) are distributed learning paradigms that train UE models with enhanced privacy, each offering different trade-offs between noise robustness and learning speed. To mitigate their respective weaknesses, we propose a hybrid federated learning (HFL) framework in which each user equipment (UE) transmits either gradients or logits, and the base station (BS) selects the per-round weights of FL and FD updates. We derive convergence of HFL framework and introduce two methods to exploit degrees of freedom (DoF) in HFL, which are (i) adaptive UE clustering via Jenks optimization and (ii) adaptive weight selection via a damped Newton method. Numerical results show that HFL achieves superior test accuracy at low SNR when both DoF are exploited.

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