Proceedings of the ACM SIGCOMM 2025 Posters and Demos
NEBULA - Decentralized Federated Learning for Heterogeneous Networks
Federated learning (FL) enables collaborative model training without sharing raw data, which is pivotal for maintaining privacy. However, existing FL frameworks often rely on a central coordinator, posing risks in het...
Abstract
Federated learning (FL) enables collaborative model training without sharing raw data, which is pivotal for maintaining privacy. However, existing FL frameworks often rely on a central coordinator, posing risks in heterogeneous networks. This work presents NEBULA, a decentralized FL platform that unifies centralized and peer-to-peer FL paradigms, integrating network awareness and autonomous adaptation for improved resilience and efficiency. Key contributions include: (1) a unified architecture supporting both server-coordinated and fully decentralized operation; (2) network-aware orchestration for dynamic communication and aggregation optimization; and (3) built-in mechanisms for robust operation. The demonstration will showcase real-time performance, defense against adversarial attacks, and adaptive client participation in challenging network scenarios.
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