A systematic review of federated learning from clients’ perspective: challenges and solutions
Abstract
Abstract Federated learning (FL) is a machine learning approach that decentralizes data and its processing by allowing clients to train intermediate models on their devices with locally stored data. It aims to preserve privacy as only model updates are shared with a central server rather than raw data. In recent years, many reviews have evaluated FL from the system (general challenges) or server’s perspectives, ignoring the importance of clients’ perspectives. Although FL helps users have control over their data, there are many challenges arising from decentralization, specifically from the perspectives of clients who are the main contributors to FL. Therefore, in response to the gap in the literature, this study intends to explore client-side challenges and available solutions by conducting a systematic literature review on 238 primary studies. Further, we analyze if a solution identified for one type of challenge is also applicable to other challenges and if there are impacts to consider. The conclusion of this survey reveals that servers and platforms have to work with clients to address client-side challenges.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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