CALM introduces a smooth trust gating mechanism for decentralized federated learning, replacing hard filtering of teacher models with class‑wise, sample‑wise, and label‑based weighting. It allows clients with heterogeneous architectures to distill knowledge from peers without a central server or shared data, even under severe non‑IID label skew. Experiments on CIFAR‑10, SVHN, OrganAMNIST, and Google Speech Commands show that CALM consistently outperforms uniform and hard‑filtered distillation and matches or exceeds other heterogeneous‑FL methods.
By Yifan Ying, Qing Tian
arXiv:2509. 11819v2 Announce Type: replace Abstract: Federated Domain Adaptation (FDA) is a federated learning (FL) approach that improves model performance at the target client by collaborating with source clients while preserving data privacy.
By Mrinmay Sen, Ankita Das, Sidhant Nair, C Krishna Mohan
MOPD‑Router rethinks teacher routing in multi‑teacher on‑policy distillation by routing supervision over the full teacher pool at each token, eliminating the need for prompt‑level domain labels or a separate routing model. The framework offers a plug‑in interface for various metrics, and introduces ExpertAlign, which scores teachers based on how well their corrections reflect their specialized post‑training knowledge. Experiments on both unlabeled and domain‑labeled mixtures show that ExpertAlign outperforms existing methods, improving overall scores by up to 12.3% on unlabeled data and 7.8% on domain‑labeled data.
whyItMatters":"Token‑level routing enables the use of complementary supervision across domains without relying on domain labels, leading to significant performance gains in multi‑teacher distillation settings."
By Tianze Xu, Yanzhao Zheng, Zhentao Zhang, Yuanqiang Yu, Chao Ma, Jihuai Zhu, Lelun Wu, Lyumanshan Ye, Pengfei Liu, Baohua Dong, Hangcheng Zhu, Ruohui Huang, Gang Yu
The paper addresses the mismatch between learner and client data distributions in federated learning, noting that traditional client selection methods often ignore this misalignment. It introduces a dynamic, influence-aware client selection framework that uses a small proxy dataset to estimate each client's utility for the learner’s objective, prioritizing informative sources while mitigating noise and heterogeneity. Experiments on CIFAR-10 with heterogeneous partitions show the proposed method outperforms static and dynamic baselines, achieving faster convergence and higher accuracy.
By Yiming Xie, Lili Su, Ningfang Mi
The paper introduces Joint Domain-Class Federated Learning (JDFL), a lightweight, optimizer‑agnostic extension designed to address feature skew in federated learning. JDFL infers pseudo‑domains from local update signals and expands the classifier head to output joint domain‑class logits, enabling the model to capture domain‑conditioned appearance while sharing a backbone. Two supervision strategies—similarity‑aware soft‑labeling and per‑sample randomized target assignment—are proposed to train the expanded head, and experiments on domain‑shifted image benchmarks show consistent improvements in global test accuracy over standard FL methods.
By Sina Najafi, Mostafa Tavassolipour, Seyed Pooya Shariatpanahi
FLoKD is an adaptive knowledge‑distillation framework designed for federated fine‑tuning of low‑rank LLMs over wireless networks. It transmits intermediate LoRA activations instead of full parameters or token‑level logits, and uses transformer block importance scoring plus dataset selection to reduce communication. Experiments on WikiText‑103, PTB, and Dialog show a 50‑65% reduction in communication while maintaining competitive perplexity.
By Xinlu Zhang, Na Yan, Yang Su, Yansha Deng, Toktam Mahmoodi