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Found 6 papers, 1 papers with code
Secure Aggregation is Not Private Against Membership Inference Attacks
In this paper, we delve into the privacy implications of SecAgg by treating it as a local differential privacy (LDP) mechanism for each local update.
FedStruct: Federated Decoupled Learning over Interconnected Graphs
We address the challenge of federated learning on graph-structured data distributed across multiple clients.
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
Inspired by group testing, the framework leverages overlapping groups of clients to identify the presence of malicious clients in the groups via a decoding operation.
Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit
Onboard machine learning on the latest satellite hardware offers the potential for significant savings in communication and operational costs.
Efficient Node Selection in Private Personalized Decentralized Learning
Personalized decentralized learning is a promising paradigm for distributed learning, enabling each node to train a local model on its own data and collaborate with other nodes to improve without sharing any data.
Decentralized Online Bandit Optimization on Directed Graphs with Regret Bounds
We consider a decentralized multiplayer game, played over $T$ rounds, with a leader-follower hierarchy described by a directed acyclic graph.
