arXiv Machine Learning June 15, 2026 By Kannanthodath Induchoodan Ajay Menon, Christian Prehofer, Yunfei Xu, Toru Hirano

FedSPC: Shared Parameter Correction for Personalized Federated Learning

Read the original on arXiv Machine Learning →

arXiv:2606. 13748v1 Announce Type: new Abstract: Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation.

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arXiv Machine Learning

Aug 13

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.

By Mirko Konstantin, Stefan Zachow, Anirban Mukhopadhyay

arXiv Machine Learning

Sep 22

Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation

The paper introduces FedPFT, a federated learning framework that tackles the feature‑classifier mismatch problem by using personalized prompts processed through a shared self‑attention transformation module. Unlike prior methods that either degrade the feature extractor or address the mismatch only after training, FedPFT aligns local features with the global classifier during training, improving aggregation and model performance. Experiments demonstrate that FedPFT surpasses state‑of‑the‑art methods by up to 5.07%, and gains up to 7.08% when combined with collaborative contrastive learning.

By Xinghao Wu, Xuefeng Liu, Jianwei Niu, Guogang Zhu, Mingjia Shi, Shaojie Tang, Jing Yuan

fine-tuning benchmarks safety

arXiv Machine Learning

Jun 16

pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning

arXiv:2606. 16304v1 Announce Type: new Abstract: Federated unlearning (FU) enables the removal of specific data contributions from federated learning (FL) models to comply with regulations such as the General Data Protection Regulation (GDPR).

By Zhuodong Liu, Xiangyu Li, Zhihao Zhang

benchmarks safety

arXiv Machine Learning

Jun 15

Federated Learning for Feature Generalization with Convex Constraints

arXiv:2606. 14416v1 Announce Type: new Abstract: Federated learning (FL) often struggles with generalization due to heterogeneous client data.

By Dongwon Kim, Donghee Kim, Sung Kuk Shyn, Kwangsu Kim

benchmarks

arXiv Machine Learning

Aug 4

CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning

arXiv:2608. 02222v1 Announce Type: new Abstract: One-shot federated learning (OSFL) has emerged as a promising collaborative model learning framework with only a single round of communication, offering significant advantages in communication efficiency and privacy preservation.

By Zijian Jiang, Chaoli Sun, Handing Wang, Xilu Wang

efficiency benchmarks safety

arXiv AI

Aug 18

FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting

arXiv:2608. 14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy.

By Hai Anh Tran, Cuong Ta, Truong X. Tran

safety

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Related stories

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation

pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning

Federated Learning for Feature Generalization with Convex Constraints

CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning

FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting