SRFL targets scalable and resilient federated learning systems across heterogeneous compute and network environments. The project includes: FedDES, a discrete-event based performance simulation framework for federated learning systems. FedMECA, a memory-efficient and concurrent aggregation approach for scalable federated learning. Long-haul RDMA studies for geo-distributed federated learning, including simulation, modeling, and real-world testbed validation.
Jan 1, 2025
Discrete-event based performance simulation for federated learning systems across heterogeneous compute/network settings.
Jan 1, 2025