Asynchronous Federated Learning on Heterogeneous Devices: A Survey
By Chenhao Xu, Youyang Qu, Yong Xiang, Longxiang Gao. Computer Science Review, 2021
388 citations recorded by the source corpus.
Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation server based on the parameters of local models, addressing concerns about privacy leakage caused by the collection of local training data. With the growing computational and communication capacities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models is becoming a prevailing trend. Nonetheless, the synchronous aggregation strategy in the classic FL paradigm, particularly on heterogeneous devices, encounters limitations in resource utilization due to the need to wait for slow devices before aggregation in each training round. Furthermore, the uneven distribution of data across devices (i.e. data heterogeneity) in real-world scenarios adversely impacts the accuracy of the global model. Consequently, many asynchronous FL (AFL) approaches have been introduced across various application contexts to enhance efficiency, performance, privacy, and security. This survey comprehensively analyzes and summarizes existing AFL variations using a novel classification scheme, including device heterogeneity, data heterogeneity, privacy, and security on heterogeneous devices, as well as applications on heterogeneous devices. Finally, this survey reveals rising challenges and presents potentially promising research directions in this under-investigated domain.
Subfields: Artificial Intelligence, Computer Science Applications
View Paper: https://www.semanticscholar.org/paper/8c73affb7489714d80a90e8b297972803beda813
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In Artificial Intelligence
- Michael G. Rabbat
- Hongyuan Zhan
- Mani Malek
- John Nguyen
- Carole-Jean Wu
- Ashkan Yousefpour
- Kshitiz Malik
- Dzmitry Huba
In Computer Science Applications
Citations in the corpus
- Papaya: Practical, Private, and Scalable Federated Learning (2021, methodology): “AsyncFL methods have been previously explored (Xie et al., 2019; Nguyen et al., 2021; Xu et al., 2021), but none has yet been demonstrated and evaluated at scale.”