# Over-The-Air Clustered Wireless Federated Learning

arXiv: https://arxiv.org/abs/2211.03363 · HTML: https://mlsanity.com/paper/2211.03363/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 7 Nov 2022.

## Builds on (most cited first, 18 of 18)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data](https://mlsanity.com/paper/2005.11418/) (2020)
- [BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning](https://mlsanity.com/paper/1905.06731/) (2019)
- [Peer-to-peer Federated Learning on Graphs](https://mlsanity.com/paper/1901.11173/) (2019)
- [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)
- [Central Server Free Federated Learning over Single-sided Trust Social Networks](https://mlsanity.com/paper/1910.04956/) (2019)
- [Convergence of Federated Learning over a Noisy Downlink](https://mlsanity.com/paper/2008.11141/) (2020)
- [Over-the-Air Decentralized Federated Learning](https://mlsanity.com/paper/2106.08011/) (2021)
- [Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning](https://mlsanity.com/paper/1811.09904/) (2018)

## Built on by (most cited first, 1 of 1)

- [Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments](https://mlsanity.com/paper/2405.09276/) (2024)

## Abstract (arXiv)

Privacy and bandwidth constraints have led to the use of federated learning (FL) in wireless systems, where training a machine learning (ML) model is accomplished collaboratively without sharing raw data. While using bandwidth-constrained uplink wireless channels, over-the-air (OTA) FL is preferred since the clients can transmit parameter updates simultaneously to a server. A powerful server may not be available for parameter aggregation due to increased latency and server failures. In the absence of a powerful server, decentralised strategy is employed where clients communicate with their neighbors to obtain a consensus ML model while incurring huge communication cost. In this work, we propose the OTA semi-decentralised clustered wireless FL (CWFL) and CWFL-Prox algorithms, which is communication efficient as compared to the decentralised FL strategy, while the parameter updates converge to global minima as O(1/T) for each cluster. Using the MNIST and CIFAR10 datasets, we demonstrate the accuracy performance of CWFL is comparable to the central-server based COTAF and proximal constraint based methods, while beating single-client based ML model by vast margins in accuracy.