Back to ListDistributed and Federated Learning
Level
Intermediate, Broad, Algorithmic, Methodological.
This topic explores the realm of distributed and federated machine learning. communication efficiency, and scheduling. Furthermore, it also covers the domain of multi-agent systems.
Learning outcomes
Content /Knowledge
Students should be able to:
- Understand and design different deep learning architectures under a distributed environment, including edge computing.
- Understand the challenges of using a computing infrastructure, including communication network, computational resource management, fault tolerance, and privacy concerns derived.
- Understand and design different aspects of multi-agent systems, including distributed problem solving and planning, search algorithms for agents, cooperation and coordination of agents, multi-agent learning, agent negotiation, and agent-oriented software engineering
- Design and implement different optimization procedures, using distributed and federated learning in distributed computing infrastructures.
Methodologicalskills
Students should be able to:
- Derive rigorous guarantees on the learning accuracy of different algorithms using a broad range of tools.
- Derive rigorous guarantees on the computational efficiency of different algorithms.
- Implementing different strategies in a distributed computing infrastructure.
Transferrable/Application
Students should be able to:
- Work effectively with others in an interdisciplinary and/or international team.
- Design and manage individual projects.
- Clearly and succinctly communicate their ideas to technical audiences.