# Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation

arXiv: https://arxiv.org/abs/2403.01417 · HTML: https://mlsanity.com/paper/2403.01417/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 3 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [Asynchronous Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1710.06952/) (2017)
- [FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-aware Model Update](https://mlsanity.com/paper/2312.05770/) (2023)
- [Asynchronous Gradient-Push](https://mlsanity.com/paper/1803.08950/) (2018)
- [AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation](https://mlsanity.com/paper/2205.13797/) (2022)
- [VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization](https://mlsanity.com/paper/2005.08501/) (2020)
- [Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks](https://mlsanity.com/paper/2301.06646/) (2023)
- [Privacy-Preserving Deep Learning via Weight Transmission](https://mlsanity.com/paper/1809.03272/) (2018)
- [Marketplace for AI Models](https://mlsanity.com/paper/2003.01593/) (2020)
- [Asynchronous speedup in decentralized optimization](https://mlsanity.com/paper/2106.03585/) (2021)
- [A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency](https://mlsanity.com/paper/2001.09782/) (2020)

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

- [FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning](https://mlsanity.com/paper/2602.15337/) (2026)

## Abstract (arXiv)

In federated learning, the models can be trained synchronously or asynchronously. Many research works have focused on developing an aggregation method for the server to aggregate multiple local models into the global model with improved performance. They ignore the heterogeneity of the training workers, which causes the delay in the training of the local models, leading to the obsolete information issue. In this paper, we design and develop Asyn2F, an Asynchronous Federated learning Framework with bidirectional model aggregation. By bidirectional model aggregation, Asyn2F, on one hand, allows the server to asynchronously aggregate multiple local models and results in a new global model. On the other hand, it allows the training workers to aggregate the new version of the global model into the local model, which is being trained even in the middle of a training epoch. We develop Asyn2F considering the practical implementation requirements such as using cloud services for model storage and message queuing protocols for communications. Extensive experiments with different datasets show that the models trained by Asyn2F achieve higher performance compared to the state-of-the-art techniques. The experiments also demonstrate the effectiveness, practicality, and scalability of Asyn2F, making it ready for deployment in real scenarios.