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Found 4 papers, 2 papers with code
Benchmarking FedAvg and FedCurv for Image Classification Tasks
Data carry a value that might vanish when shared with others; the ability to avoid sharing the data enables industrial applications where security and privacy are of paramount importance, making it possible to train global models by implementing only local policies which can be run independently and even on air-gapped data centres.
Experimenting with Normalization Layers in Federated Learning on non-IID scenarios
Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions.
Transfer Learning via Test-Time Neural Networks Aggregation
The proposed aggregation loss allows our model to learn how trained deep network parameters can be aggregated with an aggregation operator.
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis
In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data.
