FederNet: A network and device-aware emulation platform for federated learning benchmarking

Federated Learning (FL) has emerged as a pivotal privacy-preserving machine learning paradigm, enabling collaborative model train across distributed data sources. A main issue, however, is the lack of comprehensive testing environments that can accurately emulate real-world conditions at a large scale, particularly the impact of network dynamics and device capabilities on FL algorithm performance. To this end, we introduce FederNet, a novel platform designed to facilitate the development and testing of FL algorithms with realistic network and device emulation. We show how the proposed system provides a versatile platform for researchers to evaluate the performance, robustness, and scalability of FL algorithms under diverse and configurable scenarios. We describe the FederNet architecture, detail its network and device emulation capabilities, and outline potential use cases that demonstrate its utility in advancing FL research. By bridging the gap between algorithmic development and practical deployment challenges, FederNet aims to accelerate the innovation and adoption of FL technologies.