May 14, 2026 · cs.AIJ/K move · Enter open · S save
Zixuan Shu, Tiancheng Cao, Hen-Wei Huang
School of Electrical and Electronic Engineering, Nanyang Technological University, Republic of Singapore · Lee Kong Chian School of Medicine, Nanyang Technological University, Republic of Singapore
Electrocardiogram (ECG) monitoring in Internet of Medical Things (IoMT) networks is constrained by strict data-sharing regulations and privacy concerns. Federated learning (FL) enables collaborative learning by keeping raw ECG data on devices, but frequent transmissions of high-dimensional model updates incur heavy per-round traffic over bandwidth-limited links. To alleviate this bottleneck, federated distillation (FD) replaces parameter exchange with logit-based knowledge transfer. However, the performance of FD often degrades under the non-independent and identically distributed (non-IID) and long-tailed label distributions in ECG deployments. To address these challenges, we propose a bidirectional federated knowledge distillation (BiFedKD) framework that employs an aggregation-by-distillation pipeline with temperature scaling to produce a stable global distillation signal for cross-client alignment. Experiments on the MIT-BIH Arrhythmia dataset show that BiFedKD improves accuracy and Macro-F1 over the baseline by
3.52% and
9.93%, respectively. Moreover, to reach the same Macro-F1, BiFedKD reduces communication overhead by
40% and computation cost by
71.7% compared with the baseline.