Federated Learning with Downlink Device Selection
Not on the map. Why?
Abstract (arXiv)
We study federated edge learning, where a global model is trained collaboratively using privacy-sensitive data at the edge of a wireless network. A parameter server (PS) keeps track of the global model and shares it with the wireless edge devices for training using their private local data. The devices then transmit their local model updates, which are used to update the global model, to the PS. The algorithm, which involves transmission over PS-to-device and device-to-PS links, continues until the convergence of the global model or lack of any participating devices. In this study, we consider device selection based on downlink channels over which the PS shares the global model with the devices. Performing digital downlink transmission, we design a partial device participation framework where a subset of the devices is selected for training at each iteration. Therefore, the participating devices can have a better estimate of the global model compared to the full device participation case which is due to the shared nature of the broadcast channel with the price of updating the global model with respect to a smaller set of data. At each iteration, the PS broadcasts different quantized global model updates to different participating devices based on the last global model estimates available at the devices. We investigate the best number of participating devices through experimental results for image classification using the MNIST dataset with biased distribution.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 19 of its 24 references that are in the map. First posted on arXiv 7 Jul 2021.
Builds on
19 of 24 references.
References · 19
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
- 2018Federated Learning via Over-the-Air Computationcited by 215
- 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 193
- 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
- 2019Federated Learning over Wireless Fading Channelscited by 178
- 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 162
- 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
- 2019DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compressioncited by 98
- 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
- 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 61
7 more
- 2020Federated Learning With Quantized Global Model Updatescited by 54
- 2020Blind Federated Edge Learningcited by 42
- 2019Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networkscited by 39
- 2020Convergence of Federated Learning over a Noisy Downlinkcited by 29
- 2019Energy-Aware Analog Aggregation for Federated Learning with Redundant Datacited by 23
- 2020Cooperative Learning via Federated Distillation over Fading Channelscited by 16
- 2020Communication Efficient Federated Learning over Multiple Access Channelscited by 11
Built on by
Citing papers per year: 2021: 2; 2022: 1.
3 papers.