Federated Learning Meets Random Access: Energy-Efficient Uplink Resource Allocation

[2602.01913] Federated Learning Meets Random Access: Energy-Efficient Uplink Resource Allocation Skip to main content Search Submit Donate Log in Computer Science > Networking and Internet Architecture arXiv:2602.01913 (cs) [Submitted on 2 Feb 2026] Title: Federated Learning Meets Random Access: Energy-Efficient Uplink Resource Allocation Authors: Giovanni Perin , Eunjeong Jeong , Nikolaos Pappas View PDF HTML (experimental) Abstract: Artificial intelligence-generated traffic is changing the shape of wireless networks. Specifically, as the amount of data generated to train machine learning models is massive, network resources must be carefully allocated to continue supporting standard applications. In this paper, we tackle the problem of allocating radio resources for two sets of concurrent devices communicating in uplink with a gateway over the same bandwidth. A set of devices performs federated learning (FL), and accesses the medium in FDMA, uploading periodically large models. The other set is throughput-oriented and accesses the medium via random access (RA), either with ALOHA or slotted-ALOHA protocols. We derive close-to-optimal solutions to the non-convex problem of minimizing the system energy consumption subject to FL latency and RA throughput constraints. Our solutions show that ALOHA can sustain high FL efficiency, yielding up to 48% lower consumption when the system is dominated by FL traffic. On the other hand, slotted-ALOHA becomes more efficient when RA traffic dominates, yielding 6% lower consumption. Comments: Submitted to IEEE ICC workshops for possible publication Subjects: Networking and Internet Architecture (cs.NI) Cite as: arXiv:2602.01913 [cs.NI] (or arXiv:2602.01913v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2602.01913 Focus to learn more arXiv-issued DOI via DataCite Journal reference: 2026 IEEE International Communications Conference Workshops (ICC Workshops) Related DOI : https://doi.org/10.1109/ICCWorkshops63917.2026.11586396 Focus to learn more DOI(s) linking to related resources Submission history From: Giovanni Perin [ view email ] [v1] Mon, 2 Feb 2026 10:18:46 UTC (562 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.NI < prev | next > new | recent | 2026-02 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation 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? ) 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)

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