Federated learning (FL) has emerged as a popular paradigm for distributed machine learning over decentralized
\ndata. A typical FL training task involves a !eet of client devices with private data and a centralized server for
\naggregating the global model. Data generated by FL clients, e.g., smart phones, vehicles, and cameras, is prone
\nto noise. While the impact of data noise on centralized learning (CL) is well understood, to our best knowledge
\nthere is a lack of a systematic study from this point of view for FL. In this paper, we “ll this gap by presenting
\nan empirical investigation to provide a deeper understanding regarding the impact of data noise on FL. Our
\nstudy is enabled by DataNoiseGenerator, an open-source and extensible toolkit that we developed for the
\ninjection of controlled data noise across “ve diverse data modalities: image, video, audio, text, and tabular data.
\nWethen carry out extensive experiments based on the noisy data generated by DataNoiseGenerator, and our
\nexperimental evaluation results reveal that FL is significantly more vulnerable to data noise compared to CL, in
\nterms of the quality of the trained ML models. This gap between FL and CL widens as the intensity of data
\nnoise and the proportion of noisy FL clients increase. We further present a detailed analysis to diagnose the
\nroot cause of this increased sensitivity of FL to data noise. Our analysis “nds that the aggregation performed
\nby the FL server can amplify divergent updates from FL clients trained on noisy data, thereby hindering global
\nmodel convergence. We conclude that data quality issues are a fundamental challenge for deploying robust FL
\nsystems and demand novel decentralized data cleaning mechanisms.<\/p>\n","protected":false},"excerpt":{"rendered":"
Federated learning (FL) has emerged as a popular paradigm for distributed machine learning over decentralized data. A typical FL training task involves a !eet of client devices with private data and a centralized server for aggregating the global model. Data generated by FL clients, e.g., smart phones, vehicles, and cameras, is prone to noise. While 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