mlsanityData to 7 Oct 2026

Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning

arXiv 2409.16495 ↗First posted 24 Sep 2024cs.LG cs.DCcited by 3

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Abstract (arXiv)

Federated Learning (FL) is a decentralized machine learning paradigm where models are trained on distributed devices and are aggregated at a central server. Existing FL frameworks assume simple two-tier network topologies where end devices are directly connected to the aggregation server. While this is a practical mental model, it does not exploit the inherent topology of real-world distributed systems like the Internet-of-Things. We present Flight, a novel FL framework that supports complex hierarchical multi-tier topologies, asynchronous aggregation, and decouples the control plane from the data plane. We compare the performance of Flight against Flower, a state-of-the-art FL framework. Our results show that Flight scales beyond Flower, supporting up to 2048 simultaneous devices, and reduces FL makespan across several models. Finally, we show that Flight's hierarchical FL model can reduce communication overheads by more than 60%.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 24 of its 41 references that are in the map. First posted on arXiv 24 Sep 2024.

Builds on

24 of 41 references.

References · 24

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12 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputerscited by 1
  2. 2025Advancing AI-assisted Hardware Design with Hierarchical Decentralized Training and Personalized Inference-Time Optimizationcited by 0
  3. 2026SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning Systemcited by 0