# Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneity

arXiv: https://arxiv.org/abs/2112.13926 · HTML: https://mlsanity.com/paper/2112.13926/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 27 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [On the Convergence of Local Descent Methods in Federated Learning](https://mlsanity.com/paper/1910.14425/) (2019)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations](https://mlsanity.com/paper/2103.10481/) (2021)
- [Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks](https://mlsanity.com/paper/2007.09511/) (2020)
- [Federated Learning with Communication Delay in Edge Networks](https://mlsanity.com/paper/2008.09323/) (2020)
- [Energy-Aware Stochastic UAV-Assisted Surveillance](https://mlsanity.com/paper/2004.06011/) (2020)

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

- [Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach](https://mlsanity.com/paper/2405.12046/) (2024)

## Abstract (arXiv)

Federated learning (FL) has emerged as a popular technique for distributing machine learning across wireless edge devices. We examine FL under two salient properties of contemporary networks: device-server communication delays and device computation heterogeneity. Our proposed StoFedDelAv algorithm incorporates a local-global model combiner into the FL synchronization step. We theoretically characterize the convergence behavior of StoFedDelAv and obtain the optimal combiner weights, which consider the global model delay and expected local gradient error at each device. We then formulate a network-aware optimization problem which tunes the minibatch sizes of the devices to jointly minimize energy consumption and machine learning training loss, and solve the non-convex problem through a series of convex approximations. Our simulations reveal that StoFedDelAv outperforms the current art in FL, evidenced by the obtained improvements in optimization objective.