# Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks

arXiv: https://arxiv.org/abs/2212.07356 · HTML: https://mlsanity.com/paper/2212.07356/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 42 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 29 of 29)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Optimization Methods for Large-Scale Machine Learning](https://mlsanity.com/paper/1606.04838/) (2016)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [Convex Optimization: Algorithms and Complexity](https://mlsanity.com/paper/1405.4980/) (2014)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding](https://mlsanity.com/paper/1610.02132/) (2016)
- [Revisiting Distributed Synchronous SGD](https://mlsanity.com/paper/1702.05800/) (2017)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning](https://mlsanity.com/paper/2007.07174/) (2020)
- [Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge](https://mlsanity.com/paper/2001.10402/) (2020)
- [Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling](https://mlsanity.com/paper/2112.11256/) (2021)
- [FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers](https://mlsanity.com/paper/2010.05958/) (2020)
- [Federated Learning: A Signal Processing Perspective](https://mlsanity.com/paper/2103.17150/) (2021)
- [Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks](https://mlsanity.com/paper/1910.14648/) (2019)
- [SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization](https://mlsanity.com/paper/2005.07041/) (2020)
- [New Convergence Aspects of Stochastic Gradient Algorithms](https://mlsanity.com/paper/1811.12403/) (2018)
- [Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks](https://mlsanity.com/paper/2012.05137/) (2020)
- [Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning](https://mlsanity.com/paper/1910.02214/) (2019)
- [Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning](https://mlsanity.com/paper/2103.01422/) (2021)
- [Federated Learning over Wireless IoT Networks with Optimized Communication and Resources](https://mlsanity.com/paper/2110.11775/) (2021)
- [Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning](https://mlsanity.com/paper/2106.06796/) (2021)
- [Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Scheduling](https://mlsanity.com/paper/2109.06710/) (2021)
- [Data-Quality Based Scheduling for Federated Edge Learning](https://mlsanity.com/paper/2201.11247/) (2022)
- [Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection](https://mlsanity.com/paper/2201.05762/) (2022)

## Built on by (most cited first, 20 of 20)

- [Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks](https://mlsanity.com/paper/2301.06646/) (2023)
- [Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach](https://mlsanity.com/paper/2405.12046/) (2024)
- [Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates](https://mlsanity.com/paper/2403.18375/) (2024)
- [Dynamic Scheduling for Federated Edge Learning with Streaming Data](https://mlsanity.com/paper/2305.01238/) (2023)
- [Asynchronous Online Federated Learning with Reduced Communication Requirements](https://mlsanity.com/paper/2303.15226/) (2023)
- [Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks](https://mlsanity.com/paper/2312.13611/) (2023)
- [CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance](https://mlsanity.com/paper/2404.12850/) (2024)
- [Temporal Predictive Coding for Gradient Compression in Distributed Learning](https://mlsanity.com/paper/2410.02478/) (2024)
- [Over-the-Air Federated Learning with Compressed Sensing: Is Sparsification Necessary?](https://mlsanity.com/paper/2310.03410/) (2023)
- [Asynchronous Federated Learning with Incentive Mechanism Based on Contract Theory](https://mlsanity.com/paper/2310.06448/) (2023)
- [Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation](https://mlsanity.com/paper/2403.14905/) (2024)
- [Optimal Batch Allocation for Wireless Federated Learning](https://mlsanity.com/paper/2404.02395/) (2024)
- [FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks](https://mlsanity.com/paper/2404.14811/) (2024)
- [A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated Learning](https://mlsanity.com/paper/2407.05125/) (2024)
- [The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence](https://mlsanity.com/paper/2503.04052/) (2025)
- [Adaptive Deadline and Batch Layered Synchronized Federated Learning](https://mlsanity.com/paper/2505.23973/) (2025)
- [Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Things](https://mlsanity.com/paper/2512.20627/) (2025)
- [FedAgg: Adaptive Federated Learning with Aggregated Gradients](https://mlsanity.com/paper/2303.15799/) (2023)
- [Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning](https://mlsanity.com/paper/2505.03533/) (2025)
- [Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification](https://mlsanity.com/paper/2506.07328/) (2025)

## Abstract (arXiv)

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines on-device training and server-based aggregation to train a common ML model among distributed agents. In this work, we propose an asynchronous FL design with periodic aggregation to tackle the straggler issue in FL systems. Considering limited wireless communication resources, we investigate the effect of different scheduling policies and aggregation designs on the convergence performance. Driven by the importance of reducing the bias and variance of the aggregated model updates, we propose a scheduling policy that jointly considers the channel quality and training data representation of user devices. The effectiveness of our channel-aware data-importance-based scheduling policy, compared with state-of-the-art methods proposed for synchronous FL, is validated through simulations. Moreover, we show that an ``age-aware'' aggregation weighting design can significantly improve the learning performance in an asynchronous FL setting.