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Personalized Federated Learning with First Order Model Optimization
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While federated learning traditionally aims to train a single global model across decentralized local datasets, one model may not always be ideal for all participating clients. Here we propose an alternative, where each client only federates with other relevant clients to obtain a stronger model per client-specific objectives. To achieve this personalization, rather than computing a single model average with constant weights for the entire federation as in traditional FL, we efficiently calculate optimal weighted model combinations for each client, based on figuring out how much a client can benefit from another's model. We do not assume knowledge of any underlying data distributions or client similarities, and allow each client to optimize for arbitrary target distributions of interest, enabling greater flexibility for personalization. We evaluate and characterize our method on a variety of federated settings, datasets, and degrees of local data heterogeneity. Our method outperforms existing alternatives, while also enabling new features for personalized FL such as transfer outside of local data distributions.
Forward citations
Cited by 7 Pith papers
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Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learning
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FedAH: Aggregated Head for Personalized Federated Learning
FedAH improves personalized federated learning by element-wise mixing each client's local head with the global head before local training, and it reports better accuracy than ten federated baselines on five benchmarks.
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FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction
A server-side personalized aggregation method for federated learning reduces 10-second vehicle speed prediction error by 0.8% over eleven baselines on a simulated urban driving dataset.
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A ResNet variant with group normalization, trained with federated averaging, gradient clipping, and secure aggregation, reaches about 97.8% accuracy on BloodMNIST under a claimed differential privacy budget.
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