Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning (2409.16495v1)

Abstract: 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%.

• The paper introduces Flight, a FaaS-based framework that enables hierarchical federated learning to reduce communication overhead by over 60%.
• Flight leverages multi-tier aggregation and decoupled control and data planes to scale performance, supporting up to 2048 devices versus Flower's 512.
• The framework enhances data privacy, fault tolerance, and scalability, making it ideal for realistic and distributed IoT environments.

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

The paper, "Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning," introduces a novel framework designed to address the limitations of existing Federated Learning (FL) frameworks by supporting complex hierarchical network topologies. This framework, named Flight, significantly extends the traditional two-tier FL setup, enabling more realistic and scalable implementations. The paper is authored by Nathaniel Hudson, Valerie Hayot-Sasson, Yadu Babuji, Matt Baughman, J. Gregory Pauloski, Ryan Chard, Ian Foster, and Kyle Chard.

Overview

Federated Learning is a distributed machine learning paradigm where models are trained across multiple decentralized devices. Traditional FL frameworks typically assume a simplistic two-tier structure where end devices communicate directly with a central aggregation server. This structure does not align well with real-world complex networks, such as those found in Internet-of-Things (IoT) environments. To address this, Flight introduces multi-tier hierarchical topologies, asynchronous aggregation, and separation of control and data planes.

Flight Framework

Flight is an open-source Python framework offering modular interfaces for both control and data planes—tailored to be deployed across a range of heterogeneous environments.

Key contributions of Flight include:

• Hierarchical Federated Learning (HFL): Unlike traditional FL frameworks, Flight supports HFL, where intermediate aggregators aggregate model updates from their local regions before forwarding them to the global aggregator. This architecture reduces overall communication costs and enhances data privacy.
• Function-as-a-Service (FaaS): Flight implements the FaaS paradigm for executing training and aggregation tasks, enabling dynamic and scalable resource management.
• Decoupled Planes: Flight employs ProxyStore to decouple the data plane from the control plane, enhancing scalability and performance by reducing network congestion.

Performance Analysis

The paper presents a comparative analysis of Flight with Flower, a state-of-the-art FL framework. Key numerical results include:

• Scalability: Flight demonstrated superior scalability, supporting up to 2048 simultaneous devices compared to Flower, which started to exhibit gRPC errors beyond 512 devices.
• Performance: Flight with integrated ProxyStore reduced FL makespan and communication overheads by more than 60% in large hierarchical topologies.

These results underscore Flight's utility in environments with numerous edge devices, such as IoT networks.

Practical and Theoretical Implications

The introduction of hierarchical topologies in FL, as enabled by Flight, has significant implications:

• Reduced Communication Overhead: By using intermediate aggregators, Flight significantly reduces the volume of data transmitted over the network. This reduction is especially beneficial in resource-constrained environments.
• Enhanced Data Privacy: Data privacy is considerably improved as raw data remains local; only model updates are transmitted hierarchically.
• Increased Fault Tolerance and Reliability: Intermediate aggregations mean that localized network disruptions have less impact on the overall training process.

Future Developments

Flight opens several avenues for future research and development:

• Advanced Aggregation Techniques: Exploration of more sophisticated aggregation algorithms that can further optimize performance and robustness.
• Adaptive Hierarchical Structures: Developing dynamic schemes where the hierarchical structure can adapt based on network conditions and device capabilities.
• Integration with More ML Frameworks: Extending support to other deep learning frameworks like TensorFlow or JAX could broaden the applicability of Flight.

Conclusion

The paper provides an insightful description of Flight, highlighting its ability to support complex, real-world Federated Learning scenarios better than existing frameworks. Flight’s architecture addresses both scalability and efficiency, making it a powerful tool for deploying FL in IoT and other distributed environments. Flight’s contributions to hierarchical and asynchronous FL represent significant advancements in the field, offering robust solutions for decentralized data analysis and model training.