The rapid advancement of networking technologies in distributed multi-agent systems has raised concerns about data privacy, as messages containing sensitive and private information transmitted between agents are vulnerable to interception by adversaries. Meanwhile, due to limited communication bandwidth and capacity, how to transmit less information without sacrificing system performance has also gained considerable attention. In this thesis, we focus on privacy-preserving and communication-efficient distributed optimization problems in multi-agent systems. To be specific, we propose privacy-preserving algorithms for distributed average consensus and cooperative optimization problems; and introduce an information compressed scheme for distributed competitive games.

First, with the aware...[ Read more ]

The rapid advancement of networking technologies in distributed multi-agent systems has raised concerns about data privacy, as messages containing sensitive and private information transmitted between agents are vulnerable to interception by adversaries. Meanwhile, due to limited communication bandwidth and capacity, how to transmit less information without sacrificing system performance has also gained considerable attention. In this thesis, we focus on privacy-preserving and communication-efficient distributed optimization problems in multi-agent systems. To be specific, we propose privacy-preserving algorithms for distributed average consensus and cooperative optimization problems; and introduce an information compressed scheme for distributed competitive games.

First, with the awareness of preserving agents’ privacy in a distributed system, we develop a novel push-sum approach for directed networks that can protect the privacy of all agents while achieving average consensus simultaneously. The paradigm utilizes a state decomposition mechanism to preserve the agents’ privacy. Second, for general objective functions, we propose a variant of the state decomposition mechanism, where Laplacian noise is perturbed on the substate of the gradient to achieve ∈-differential privacy for each agent. Furthermore, linear convergence is guaranteed under a constant stepsize policy. Finally, for communication-efficient distributed competitive games, we introduce a discrete-time distributed NE seeking algorithm with information compressed by general compressors before transmission. The proposed difference compression method ensures linear convergence to the Nash equilibrium of the game. Moreover, the communication cost is saved in terms of transmitting bits by adopting a general class of compressors.