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For businesses, getting the biggest ROI from collected data requires strategic, updated data management strategies.

Recent studies have demonstrated that backdoor attacks can cause a significant security threat to federated learning. Existing defense methods mainly focus on detecting or eliminating the backdoor patterns after the model is backdoored. However, these methods either cause model performance degradation or heavily rely on impractical assumptions, such as labeled clean data, which exhibit limited effectiveness in federated learning. To this end, we propose FLPurifier, a novel backdoor defense method in federated learning that can effectively purify the possible backdoor attributes before federate...

Federated learning (FL), as a machine learning framework, has garnered substantial attention from researchers in recent years. FL makes it possible to train a global model through coordination by a central server while ensuring the privacy of data on individual edge devices. However, the data on edge devices that participate in FL training are not independently and identically distributed (IID), resulting in challenges related to heterogeneity data. In this paper, we introduce the challenges generated by non-IID data to FL and provide a detailed classification of non-IID data. Then, we summari...

PALO ALTO, Calif.--(BUSINESS WIRE)--Apr 30, 2024--

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