# Decentralized Federated Learning via SGD over Wireless D2D Networks

arXiv: https://arxiv.org/abs/2002.12507 · HTML: https://mlsanity.com/paper/2002.12507/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2020.

## Built on by (most cited first, 30 of 33)

- [Decentralized Federated Averaging](https://mlsanity.com/paper/2104.11375/) (2021)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis](https://mlsanity.com/paper/2101.12704/) (2021)
- [Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations](https://mlsanity.com/paper/2103.10481/) (2021)
- [Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems](https://mlsanity.com/paper/2101.03367/) (2021)
- [On the (In)security of Peer-to-Peer Decentralized Machine Learning](https://mlsanity.com/paper/2205.08443/) (2022)
- [Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey](https://mlsanity.com/paper/2302.02573/) (2023)
- [Over-the-Air Decentralized Federated Learning](https://mlsanity.com/paper/2106.08011/) (2021)
- [Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks](https://mlsanity.com/paper/2202.02947/) (2022)
- [Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications](https://mlsanity.com/paper/2207.11812/) (2022)
- [Asynchronous Decentralized Learning over Unreliable Wireless Networks](https://mlsanity.com/paper/2202.00955/) (2022)
- [Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing](https://mlsanity.com/paper/2203.09670/) (2022)
- [Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction](https://mlsanity.com/paper/2308.04237/) (2023)
- [A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning](https://mlsanity.com/paper/2103.10346/) (2021)
- [Confederated Learning: Federated Learning with Decentralized Edge Servers](https://mlsanity.com/paper/2205.14905/) (2022)
- [A Comprehensive Data-centric Overview of Federated Graph Learning](https://mlsanity.com/paper/2507.16541/) (2025)
- [Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying](https://mlsanity.com/paper/2206.04742/) (2022)
- [Communication-Efficient Design for Quantized Decentralized Federated Learning](https://mlsanity.com/paper/2303.08423/) (2023)
- [The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication](https://mlsanity.com/paper/2103.13026/) (2021)
- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning](https://mlsanity.com/paper/2501.09320/) (2025)
- [Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping](https://mlsanity.com/paper/2305.11135/) (2023)
- [UAV-Aided Decentralized Learning over Mesh Networks](https://mlsanity.com/paper/2203.01008/) (2022)
- [To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing](https://mlsanity.com/paper/2209.02166/) (2022)
- [Data Games: A Game-Theoretic Approach to Swarm Robotic Data Collection](https://mlsanity.com/paper/2303.03602/) (2023)
- [Collaborative Distributed Machine Learning](https://mlsanity.com/paper/2309.16584/) (2023)
- [Byzantine-Robust Federated Learning: An Overview With Focus on Developing Sybil-based Attacks to Backdoor Augmented Secure Aggregation Protocols](https://mlsanity.com/paper/2410.22680/) (2024)
- [FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classification](https://mlsanity.com/paper/2503.18981/) (2025)
- [Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling](https://mlsanity.com/paper/2109.03350/) (2021)
- [The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistencies](https://mlsanity.com/paper/2310.07471/) (2023)

## Abstract (arXiv)

Federated Learning (FL), an emerging paradigm for fast intelligent acquisition at the network edge, enables joint training of a machine learning model over distributed data sets and computing resources with limited disclosure of local data. Communication is a critical enabler of large-scale FL due to significant amount of model information exchanged among edge devices. In this paper, we consider a network of wireless devices sharing a common fading wireless channel for the deployment of FL. Each device holds a generally distinct training set, and communication typically takes place in a Device-to-Device (D2D) manner. In the ideal case in which all devices within communication range can communicate simultaneously and noiselessly, a standard protocol that is guaranteed to converge to an optimal solution of the global empirical risk minimization problem under convexity and connectivity assumptions is Decentralized Stochastic Gradient Descent (DSGD). DSGD integrates local SGD steps with periodic consensus averages that require communication between neighboring devices. In this paper, wireless protocols are proposed that implement DSGD by accounting for the presence of path loss, fading, blockages, and mutual interference. The proposed protocols are based on graph coloring for scheduling and on both digital and analog transmission strategies at the physical layer, with the latter leveraging over-the-air computing via sparsity-based recovery.