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Quantum Federated Learning in Distributed Systems: Challenges and Future Directions
This project reviews quantum federated learning (QFL) for non-IID and heterogeneous distributed systems. Practical federated learning systems often involve clients with different data distributions, device capabilities, and communication conditions, which can lead to unstable aggregation and degraded global performance. QFL provides a promising direction by combining federated learning with quantum neural networks and parameterized quantum circuits.
The project will organize recent QFL studies from the perspectives of aggregation strategies, quantum circuit architectures, data encoding, communication cost, and device heterogeneity. It will also discuss future research directions for architecture-aware QFL, including the potential role of quantum neural architecture search (QNAS) for adapting quantum circuit structures to different clients, data distributions, and resource constraints.
Faculty Supervisor:
Trung Q. Duong
Student:
Partner:
Korea University
Discipline:
Computer science
Sector:
Artificial Intelligence; Quantum Science and Technology
University:
Memorial University of Newfoundland
Program:
Globalink Research Award
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.