Private Learning on Networks: Part II

Understand

This paper considers a distributed multi-agent optimization problem, with the global objective consisting of the sum of local objective functions of the agents.

• The agents solve the optimization problem using local computation and communication between adjacent agents in the network.
• We present two randomized iterative algorithms for distributed optimization.
• To improve privacy, our algorithms add "structured" randomization to the information exchanged between the agents.