Seminar Details
2022-10-04 (14h) : Distributed Coordination and Optimization with Privacy Guarantees and Quantized Communication
At Euler building (room a.002)
Organized by Mathematical Engineering
Speaker :
Apostolos Rikos (Division of Decision and Control Systems, KTH Royal Institute of Technology, Sweden)
Abstract :
A distributed system (or network) can be viewed as a set of subsystems that can share information via interconnection links, which form a generally directed communication topology. Distributed systems prove to be of vital importance for the effectiveness of performing various tasks in the areas of cooperative control, distributed coordination, and control of multicomponent systems. This presentation concerns novel distributed algorithms for (i) distributed averaging, (ii) resource allocation, and (iii) privacy preservation. During the operation of the proposed algorithms, each node operates over a directed communication topologies, processes and transmits quantized information and converges in finite time.In the first part of this presentation, we focus on the distributed quantized average consensus problem. In this problem, each node is associated with some initial quantized value. The goal is for the nodes to obtain the average of these initial values (or some value close to the average). We present and analyze a novel distributed averaging algorithm which operates exclusively with quantized values (the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and rely on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage).In the second part of this presentation, we focus on the resource allocation problem. In this problem, each node is associated with a convex local cost function. The goal is for the nodes is to cooperatively minimize a common additive cost function. More specifically, nodes aim to to calculate a set of parameters that minimize the global cost function which is the summation of the local cost functions. We present and analyze a novel distributed resource allocation algorithm which operates exclusively with quantized values and rely on event-driven updates.In the third part of this presentation, we focus on privacy preservation strategies for the algorithms presented in the previous two parts. A node is said to preserve the privacy of its initial state if its value cannot be inferred by any curious node in the network and also curious nodes are not able to determine a finite range in which this value belongs. We propose privacy preservation strategies which operate in an event-driven fashion, and are adjusted to the quantized nature of the proposed algorithms in the first two parts of this presentation.