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Federated Lightweight Fine-Tuning

BACKGROUND

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23 Jul

Federated Lightweight Fine-Tuning

REPORT · WHAT · ArXiv CS.AI

Other feeds and industry voices

27 Mar

Agentic Trust Coordination for Federated Learning through Adaptive Thresholding and Autonomous Decision Making in Sustainable and Resilient Industrial Networks

arXiv:2603.25334v1 Announce Type: new Abstract: Distributed intelligence in industrial networks increasingly integrates sensing, communication, and computation across

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3 Jul

Auto-FL-Research: Agentic Search for Federated Learning Algorithms

arXiv:2607.01366v1 Announce Type: new Abstract: Federated learning (FL) research often depends on many small but consequential algorithmic choices: optimizer variants, server

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27 Apr

Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning

arXiv:2604.22072v1 Announce Type: cross Abstract: Federated learning (FL) aggregation on serverless platforms faces a hard scalability ceiling: existing architectures (lambda-FL,

REPORT · WHEN · ArXiv CS.AIOPEN STORY

2 Apr

Task-Centric Personalized Federated Fine-Tuning of Language Models

arXiv:2604.00050v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising technique for training language models on distributed and private datasets of

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9 Jul

WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning

arXiv:2607.06616v1 Announce Type: cross Abstract: Label skew in federated learning (FL) causes client drift and degrades global accuracy. Synthetic data augmentation can reduce

REPORT · WHERE · ArXiv CS.AIOPEN STORY

3 Aug

Evaluating Federated Pre-Training: On the Reliability of Downstream Fine-Tuning and Intrinsic Evaluation

arXiv:2607.28658v1 Announce Type: cross Abstract: Federated pre-training offers a way to train foundation models on private or distributed data without centralizing the underlying

REPORT · WHAT · ArXiv CS.AIOPEN STORY

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