Distributed Parallel and Federated Optimization

FedCE: Federated Certainty Equivalence Control for Linear Gaussian Systems

By Angela Berti13 August 2024FoundationsDistributed Parallel and Federated Optimization, Foundational Research

FedCE: Federated Certainty Equivalence Control for Linear Gaussian Systems Decentralized multi-agent systems are ubiquitous across various applications, such as decentralized control of robots and drones, decentralized autonomous vehicles, and non-cooperative games, among others. Extensive research in the literature has focused on decentralized multi-agent systems with known system dynamics, exploring various frameworks, such as decentralized optimal […]

Personalized Federated Learning via Data-centric Regularization

By Angela Berti12 August 2024FoundationsDistributed Parallel and Federated Optimization, Foundational Research

Personalized Federated Learning via Data-centric Regularization Federated learning is a large-scale machine learning training paradigm where data is distributed across clients, and can be highly heterogeneous from one client to another. To ensure personalization in client models, and at the same time to ensure that the local models have enough commonality (i.e., prevent “client-drift”), it […]

Parallelization

By Angela Berti12 August 2024FoundationsDistributed Parallel and Federated Optimization, Foundational Research

Parallelization A major challenge in Federated Learning is tackling the behavior of Byzantine machines, which behave completely arbitrarily. This can happen due to software or hardware crashes, poor communication links between local machines and the center machine, stalled computations, and even coordinated or malicious attacks by a third party. In order to fit complex machine […]