# Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints

arXiv: https://arxiv.org/abs/2606.28342 · HTML: https://mlsanity.com/paper/2606.28342/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Decentralized Federated Averaging](https://mlsanity.com/paper/2104.11375/) (2021)
- [Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks](https://mlsanity.com/paper/1912.13163/) (2019)
- [SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication](https://mlsanity.com/paper/2210.14026/) (2022)
- [Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications](https://mlsanity.com/paper/2103.13266/) (2021)
- [Impact of network topology on the performance of Decentralized Federated Learning](https://mlsanity.com/paper/2402.18606/) (2024)
- [MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing](https://mlsanity.com/paper/2002.02897/) (2020)
- [Robustness of Decentralised Learning to Nodes and Data Disruption](https://mlsanity.com/paper/2405.02377/) (2024)
- [Decentralized Federated Learning Over Imperfect Communication Channels](https://mlsanity.com/paper/2405.12894/) (2024)
- [DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networks](https://mlsanity.com/paper/2406.13533/) (2024)
- [Mobility-Aware Decentralized Federated Learning with Joint Optimization of Local Iteration and Leader Selection for Vehicular Networks](https://mlsanity.com/paper/2503.06443/) (2025)
- [Initialisation and Network Effects in Decentralised Federated Learning](https://mlsanity.com/paper/2403.15855/) (2024)

## 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.