# On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach

arXiv: https://arxiv.org/abs/2605.26162 · HTML: https://mlsanity.com/paper/2605.26162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 24 May 2026.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [Stochastic Gradient Push for Distributed Deep Learning](https://mlsanity.com/paper/1811.10792/) (2018)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Distributed optimization over time-varying directed graphs](https://mlsanity.com/paper/1303.2289/) (2013)
- [DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training](https://mlsanity.com/paper/2206.00187/) (2022)
- [FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-aware Model Update](https://mlsanity.com/paper/2312.05770/) (2023)
- [SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication](https://mlsanity.com/paper/2210.14026/) (2022)
- [Efficient and Secure Federated Learning for Financial Applications](https://mlsanity.com/paper/2303.08355/) (2023)
- [Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees](https://mlsanity.com/paper/2402.03448/) (2024)
- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://mlsanity.com/paper/2109.03999/) (2021)
- [SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead](https://mlsanity.com/paper/2406.00431/) (2024)
- [A2CiD2: Accelerating Asynchronous Communication in Decentralized Deep Learning](https://mlsanity.com/paper/2306.08289/) (2023)
- [Boosting Asynchronous Decentralized Learning with Model Fragmentation](https://mlsanity.com/paper/2410.12918/) (2024)
- [DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networks](https://mlsanity.com/paper/2406.13533/) (2024)
- [Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates](https://mlsanity.com/paper/2403.18375/) (2024)

## Abstract (arXiv)

Asynchronous decentralized federated learning (ADFL) eliminates central coordination and global synchronization, making it attractive for large-scale and heterogeneous systems. However, frequent peer-to-peer communication, asynchronous updates on directed topologies, and non-IID data jointly lead to excessive communication overhead, biased aggregation and severe model drift. We propose PushCen-ADFL, a communication-efficient ADFL framework that enables stable training under asymmetric communication and delayed client participation. PushCen-ADFL couples communication, aggregation, and local stabilization in a shared centroid representation space, forming a closed loop between compression and optimization. Clients exchange centroid-form messages, apply average-preserving push-sum mixing to correct aggregation bias, and use a lightweight centroid regularization anchored in the same centroid space to mitigate drift under heterogeneity and staleness. A bounded, sender-deduplicated buffer further improves robustness under irregular asynchronous arrivals. Experiments on vision datasets demonstrate that PushCen-ADFL improves accuracy under data heterogeneity by up to 6% while reducing per-push communication cost by more than 80%, achieving a favorable accuracy-communication trade-off.