Researchers from KAUST and Sony AI Propose FedP3: A Machine Learning-based Solution Designed to Tackle both Data and Model Heterogeneities while Prioritizing Privacy
Researchers from Sony AI and KAUST have introduced FedP3 to address the challenge of federated learning (FL) in scenarios where devices possess varying capabilities and data distributions, known as model heterogeneity. FL involves training a global model using data stored locally on each device, ensuring privacy. However, accommodating these differences in devices and data distributions escalates the complexity of FL implementations. The researchers aimed to resolve the issue of client-side model heterogeneity, where devices differ in memory storage, processing capabilities, and network bandwidth.
Practical Solutions and Value:
FedP3 offers practical solutions to the challenges of federated learning by integrating personalized model creation, dual pruning strategies, and privacy-preserving mechanisms. It allows for the creation of unique models for each client, optimizes model size and efficiency through global and local pruning techniques, and ensures client privacy by minimizing the data shared with the server. FedP3’s effectiveness in reducing communication costs and maintaining performance across various datasets and model architectures has been validated through extensive experimental studies.
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