mlsanityData to 7 Oct 2026

Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints

arXiv 2606.28342 ↗First posted 1 Jun 2026cs.NI cs.AI cs.MAcited by 0

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Abstract (arXiv)

Decentralized learning is a promising paradigm for collaborative training in mobile and pervasive systems, as it avoids a central coordinator and does not require sharing raw data. Yet, most analyses rely on idealized communication assumptions that break down in wireless settings, where connectivity is intermittent, topology changes due to mobility, and bandwidth is limited. We study decentralized averaging under client asynchrony, time-varying contact graphs, and technology-dependent throughput constraints. We implement a fully decentralized protocol that overlaps synchronization with local training and supports partial tensor-level transfers when contacts end early. Using Random Waypoint mobility and multiple wireless technologies (Bluetooth LE, LTE, and Wi-Fi), we quantify how network dynamics and link capacity impact convergence. We identify three operating regimes: (i) inter-contact time largely dictates convergence via mixing, (ii) partial updates are often well tolerated when contacts are frequent, and (iii) very dense contact patterns can trigger contention, reducing effective throughput. These findings provide a practical lens to reason about decentralized learning deployments over realistic wireless systems, highlighting when improving connectivity, increasing bandwidth, or mitigating contention is most impactful.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 26 references that are in the map. First posted on arXiv 1 Jun 2026.

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15 of 26 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  5. 2021Decentralized Federated Averagingcited by 114
  6. 2019Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networkscited by 79
  7. 2022SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communicationcited by 21
  8. 2021Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applicationscited by 11
  9. 2024Impact of network topology on the performance of Decentralized Federated Learningcited by 7
  10. 2020MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensingcited by 5
  11. 2024Robustness of Decentralised Learning to Nodes and Data Disruptioncited by 5
  12. 2024Decentralized Federated Learning Over Imperfect Communication Channelscited by 5
3 more
  1. 2024DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networkscited by 5
  2. 2025Mobility-Aware Decentralized Federated Learning with Joint Optimization of Local Iteration and Leader Selection for Vehicular Networkscited by 5
  3. 2024Initialisation and Network Effects in Decentralised Federated Learningcited by 2

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