arXiv Computer Vision
Unapologetically Distributed: A Call for Decentralized Document Analysis
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Federated Large Language Models: Current Progress and Future Directions
arXiv:2409. 15723v3 Announce Type: replace Abstract: Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy and governance concerns.
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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.
arXiv AI
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FedSkillGit: Federated Skill Evolution via Version Control
FedSkillGit is a federated framework that enables large language model agents to evolve reusable natural‑language skills without sharing raw data. Instead of uploading entire skill documents, clients submit bounded patches and local experience summaries, which are filtered for privacy, deduplicated, and merged into a global skill candidate. The server distributes the candidate for client‑side validation, publishing it only when performance improves, and experiments show it outperforms baselines by up to 22% while approaching centralized results.
arXiv AI
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Vision Paper: Why Distributed LLM Post-Training and Decentralized Alignment Are Now Essential
The paper argues that distributed learning—federated and decentralized—should be a core approach for large language model post‑training, rather than an optional privacy measure. It critiques current methods like RLHF and DPO for assuming centralized preference data, citing scientific, practical, and ethical flaws. The authors present a three‑pillar framework (privacy and data sovereignty, pluralism and representational equity, robustness and scalability), formalize technical challenges such as reference drift and preference heterogeneity, and outline algorithmic directions and a research agenda to make distributed post‑training feasible.
arXiv AI
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Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages
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arXiv AI
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Federated Prompt Learning: A Unified Framework, Empirical Analysis, and Future Directions
arXiv:2608. 13844v1 Announce Type: cross Abstract: Large language models (LLMs) have become core components of cloud-based intelligent services in academia and industry, yet their training and deployment are hindered by high computational costs, data centralization, and privacy concerns.