arXiv Computation and Language September 22, 2026 By Kiran Naseer, Samreen Azhar, Umar Shoaib, Haroon Mahmood, Muhammad Awais

Perplexity Predicts Protection: Choosing Pretrained Backbones for Worst-Client Fairness in Federated Parameter-Efficient Fine-Tuning

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arXiv Machine Learning

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When More Parameters Hurt: Foundation Model Priors Amplify Worst-Client Disparity Under Extreme Federated Heterogeneity

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arXiv AI

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arXiv AI

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From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning

arXiv:2606. 10595v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning.

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arXiv Machine Learning

Jun 16

Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts

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arXiv AI

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FedRAW: Preserving Rare-Label Influence in Asynchronous Federated Learning

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arXiv Machine Learning

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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

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When More Parameters Hurt: Foundation Model Priors Amplify Worst-Client Disparity Under Extreme Federated Heterogeneity

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