M11 LONDON

Course website https://sites.google.com/view/eeci-igsc-2026-multi-agent-opt Recent technological advances have enabled the widespread adoption of intelligent devices in many applications, such as healthcare, edge computing, transportation, robotics, smart grids. These devices are equipped with communication and computational resources, which allow them to learn from the data they collect. However, in order to improve the accuracy of the models they train, the important paradigm of decentralized learning is being deployed. Therefore, there is a need for algorithmic advances that can support cooperative learning and optimization. The course will provide a thorough introduction to the state of the art in decentralized learning, both with federated and peer-to-peer communication architectures. The course will cover different algorithmic approaches, e.g. gradient-based and dual methods. A particular emphasis will be given to the practical challenges that arise in this context, such as asynchrony and limited communications. Outline 1. Introduction and motivating examples (healthcare, edge/fog computing, transportation, robotics, smart grids) 2. Decentralized learning and optimization • From centralized to decentralized • Practical challenges • Decentralized cooperative architectures 3. Federated learning • Deep learning • Privacy and resilience to attacks 4. Consensus and distributed optimization • The consensus algorithm: standard, accelerated, push-sum/ratio, broadcast w/ faulty communications • Consensus-based distributed optimization: gradient tracking and Newton • Non-expansive operators for optimization: background, operator-based algorithms (proximal gradient, ADMM, primal-dual,…) • Application to decentralized asynchronous and lossy networks: a stochastic operators approach 5. Current trends • Reinforcement learning, Online distributed optimization, Data-driven optimization, Privacy, Human-in-the-loop • Frontiers in applications 6. Hands-on coding experiences