FDA-Opt: Federated Fine-Tuning via Dynamic Update Schedules

[2505.04535] FDA-Opt: Federated Fine-Tuning via Dynamic Update Schedules Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2505.04535 (cs) [Submitted on 7 May 2025 ( v1 ), last revised 14 Aug 2026 (this version, v4)] Title: FDA-Opt: Federated Fine-Tuning via Dynamic Update Schedules Authors: Michael Theologitis , Vasilis Samoladas , Antonios Deligiannakis View a PDF of the paper titled FDA-Opt: Federated Fine-Tuning via Dynamic Update Schedules, by Michael Theologitis and 2 other authors View PDF HTML (experimental) Abstract: Federated Learning (FL) enables the utilization of vast, previously inaccessible data sources. At the same time, pre-trained Language Models (LMs) have taken the world by storm and for good reason. They exhibit remarkable emergent abilities and are readily adapted to downstream tasks. This opens one of the most exciting frontiers in FL: fine-tuning LMs. Yet, a persistent challenge in FL is the frequent, rigid communication of parameters -- a problem magnified by the sheer size of these contemporary models. The FedOpt family of algorithms has become the go-to approach for FL, relying on fixed but arbitrary intervals for model exchanges. Recently, the FDA algorithm prescribed a dynamic approach by monitoring the training progress. However, it introduced a hard-to-calibrate parameter and imposed a rigid synchronization scheme. In this work, we address these limitations by proposing the FDA-Opt family of algorithms -- a unified generalization of both FDA and FedOpt. Our experimental evaluation focuses on fine-tuning LMs on downstream NLP tasks and demonstrates that FDA-Opt outperforms FedOpt even when it is configured with hyper-parameters specifically optimized for the latter. In other words, we show that FDA-Opt is a practical, drop-in replacement for FedOpt in modern FL libraries and systems: it requires no additional configuration and delivers superior performance out of the box. Comments: CIKM 2026 Subjects: Machine Learning (cs.LG) ; Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2505.04535 [cs.LG] (or arXiv:2505.04535v4 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2505.04535 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1145/3799682.3840785 Focus to learn more DOI(s) linking to related resources Submission history From: Michael Theologitis [ view email ] [v1] Wed, 7 May 2025 16:13:21 UTC (27,069 KB) [v2] Tue, 24 Jun 2025 16:20:46 UTC (13,349 KB) [v3] Thu, 14 May 2026 17:58:08 UTC (12,927 KB) [v4] Fri, 14 Aug 2026 22:34:01 UTC (1,677 KB) Full-text links: Access Paper: View a PDF of the paper titled FDA-Opt: Federated Fine-Tuning via Dynamic Update Schedules, by Michael Theologitis and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2025-05 Change to browse by: cs cs.DC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• HTN Planning as a Coordination Layer for Multi-Server MCP Tool Orchestration#1118804
• Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation#299893
• Automatic Model-Hardware Co-Adaptation for Heterogeneous AI Accelerators#820550
• Event-Driven App Design for High-Concurrency Microservices#88147
• Substrate-Portable Execution for Production LLM Workflows#973734