Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints
For practitioners deploying decentralized learning in mobile/wireless systems, this work provides a practical framework to understand when improving connectivity, bandwidth, or contention mitigation is most impactful.
This paper studies decentralized learning under realistic wireless constraints (mobility, intermittent connectivity, limited bandwidth) and identifies three operating regimes that dictate convergence behavior, showing that inter-contact time, partial update tolerance, and contention effects are key factors.
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.