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Found 5 papers, 0 papers with code
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy
This paper investigates the use of the cubic-regularized Newton method within a federated learning framework while addressing two major concerns that commonly arise in federated learning: privacy leakage and communication bottleneck.
Communication-efficient and Differentially-private Distributed Nash Equilibrium Seeking with Linear Convergence
Due to the nature of distributed systems, privacy and communication efficiency are two critical concerns.
Compression-based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games
To jointly address these issues, we propose an algorithm that uses stochastic compression to save communication resources and conceal information through random errors induced by compression.
Network Learning in Quadratic Games from Fictitious Plays
Next, in view of the inherent stability and sparsity constraints for the network interaction structure, we propose a stable and sparse system identification framework for learning the interaction graph from full player action observations.
Privacy-Preserving Push-sum Average Consensus via State Decomposition
That is to say, only the exchanged substate would be visible to an adversary, preventing the initial state information from leakage.
