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Found 5 papers, 0 papers with code
Fake or Compromised? Making Sense of Malicious Clients in Federated Learning
Federated learning (FL) is a distributed machine learning paradigm that enables training models on decentralized data.
FedPerm: Private and Robust Federated Learning by Parameter Permutation
We present FedPerm, a new FL algorithm that addresses both these problems by combining a novel intra-model parameter shuffling technique that amplifies data privacy, with Private Information Retrieval (PIR) based techniques that permit cryptographic aggregation of clients' model updates.
E2FL: Equal and Equitable Federated Learning
Federated Learning (FL) enables data owners to train a shared global model without sharing their private data.
FRL: Federated Rank Learning
The FRL server uses a voting mechanism to aggregate the parameter rankings submitted by clients in each training epoch to generate the global ranking of the next training epoch.
FSL: Federated Supermask Learning
FSL clients share local subnetworks in the form of rankings of network edges; more useful edges have higher ranks.
