# Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning

arXiv: https://arxiv.org/abs/2107.11415 · HTML: https://mlsanity.com/paper/2107.11415/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 17 references that are in the map.
- First posted on arXiv 23 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [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)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [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)
- [Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge](https://mlsanity.com/paper/2001.10402/) (2020)
- [Federated Learning: A Signal Processing Perspective](https://mlsanity.com/paper/2103.17150/) (2021)
- [Staleness-aware Async-SGD for Distributed Deep Learning](https://mlsanity.com/paper/1511.05950/) (2015)
- [Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks](https://mlsanity.com/paper/1910.14648/) (2019)
- [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)

## Built on by (most cited first, 7 of 7)

- [Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks](https://mlsanity.com/paper/2301.06646/) (2023)
- [Pisces: Efficient Federated Learning via Guided Asynchronous Training](https://mlsanity.com/paper/2206.09264/) (2022)
- [Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO](https://mlsanity.com/paper/2212.06482/) (2022)
- [Asynchronous Online Federated Learning with Reduced Communication Requirements](https://mlsanity.com/paper/2303.15226/) (2023)
- [Communication Trade-offs in Federated Learning of Spiking Neural Networks](https://mlsanity.com/paper/2303.00928/) (2023)
- [The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence](https://mlsanity.com/paper/2503.04052/) (2025)
- [FedAvgen: Metadata for Model Aggregation In Communication Systems](https://mlsanity.com/paper/2505.05486/) (2025)

## Abstract (arXiv)

Federated Learning (FL) is a newly emerged decentralized machine learning (ML) framework that combines on-device local training with server-based model synchronization to train a centralized ML model over distributed nodes. In this paper, we propose an asynchronous FL framework with periodic aggregation to eliminate the straggler issue in FL systems. For the proposed model, we investigate several device scheduling and update aggregation policies and compare their performances when the devices have heterogeneous computation capabilities and training data distributions. From the simulation results, we conclude that the scheduling and aggregation design for asynchronous FL can be rather different from the synchronous case. For example, a norm-based significance-aware scheduling policy might not be efficient in an asynchronous FL setting, and an appropriate "age-aware" weighting design for the model aggregation can greatly improve the learning performance of such systems.