# Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation

arXiv: https://arxiv.org/abs/2210.01161 · HTML: https://mlsanity.com/paper/2210.01161/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 24 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning](https://mlsanity.com/paper/1807.06629/) (2018)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Tighter Theory for Local SGD on Identical and Heterogeneous Data](https://mlsanity.com/paper/1909.04746/) (2019)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2102.06387/) (2021)
- [Decentralized Deep Learning with Arbitrary Communication Compression](https://mlsanity.com/paper/1907.09356/) (2019)
- [Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning](https://mlsanity.com/paper/2206.08307/) (2022)
- [Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays](https://mlsanity.com/paper/2206.07638/) (2022)
- [Advances in Asynchronous Parallel and Distributed Optimization](https://mlsanity.com/paper/2006.13838/) (2020)
- [An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization](https://mlsanity.com/paper/1505.04824/) (2015)
- [Asynchronous Federated Learning with Differential Privacy for Edge Intelligence](https://mlsanity.com/paper/1912.07902/) (2019)

## Built on by (most cited first, 12 of 12)

- [FADAS: Towards Federated Adaptive Asynchronous Optimization](https://mlsanity.com/paper/2407.18365/) (2024)
- [Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability](https://mlsanity.com/paper/2409.17446/) (2024)
- [Asynchronous Heterogeneous Linear Quadratic Regulator Design](https://mlsanity.com/paper/2404.09061/) (2024)
- [Quantized and Asynchronous Federated Learning](https://mlsanity.com/paper/2410.00242/) (2024)
- [Convergence Analysis of Decentralized ASGD](https://mlsanity.com/paper/2309.03754/) (2023)
- [Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays](https://mlsanity.com/paper/2405.10123/) (2024)
- [FAVANO: Federated AVeraging with Asynchronous NOdes](https://mlsanity.com/paper/2305.16099/) (2023)
- [Dual-Delayed Asynchronous SGD for Arbitrarily Heterogeneous Data](https://mlsanity.com/paper/2405.16966/) (2024)
- [Serverless Federated Learning with flwr-serverless](https://mlsanity.com/paper/2310.15329/) (2023)
- [Analysis of Asynchronous Federated Learning: Unraveling the Interactions between Gradient Compression, Delay, and Data Heterogeneity](https://mlsanity.com/paper/2504.19903/) (2025)
- [Stragglers Can Contribute More: Uncertainty-Aware Distillation for Asynchronous Federated Learning](https://mlsanity.com/paper/2511.19966/) (2025)
- [Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks](https://mlsanity.com/paper/2609.32832/) (2026)

## Abstract (arXiv)

Synchronous updates may compromise the efficiency of cross-device federated learning once the number of active clients increases. The textitFedBuff algorithm (Nguyen et al., 2022) alleviates this problem by allowing asynchronous updates (staleness), which enhances the scalability of training while preserving privacy via secure aggregation. We revisit the textitFedBuff algorithm for asynchronous federated learning and extend the existing analysis by removing the boundedness assumptions from the gradient norm. This paper presents a theoretical analysis of the convergence rate of this algorithm when heterogeneity in data, batch size, and delay are considered.