FedDES: Discrete Event Based Performance Simulation for Federated Learning Systems

Wed, 01 Jan 2025 00:00:00 +0000

FedDES models federated learning training, communication, and aggregation as lightweight events, enabling systematic performance analysis of complex FL workflows under diverse networking conditions.

Scalable, Resilient Federated Learning

Wed, 01 Jan 2025 00:00:00 +0000

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.

Related publications:

FedDES: Discrete Event Based Performance Simulation for Federated Learning Systems
FedMECA: Scalable Federated Learning via Memory-Efficient and Concurrent Aggregation
When RDMA Goes Long-Haul: Characterization, Modeling, and Verbs-Level Emulation with Implications for Federated Learning
Can Long-Haul RDMA Benefit Federated Learning?

SimGrid | Zhonghao Chen

FedDES: Discrete Event Based Performance Simulation for Federated Learning Systems

Scalable, Resilient Federated Learning