Towards Federated Learning at Scale: System Design (1902.01046v2)

Abstract: Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.

• The paper presents a scalable federated learning approach using synchronous FedAvg and secure aggregation protocols, validated through mobile deployments.
• It details a modular system design with an on-device FL runtime and an Actor model–based server architecture to efficiently manage model updates.
• The system ensures data privacy through differential privacy, attestation, and multi-tenancy, demonstrating its practicality in large-scale applications.

Towards Federated Learning at Scale: System Design

The paper "Towards Federated Learning at Scale: System Design" presents a comprehensive overview of a scalable federated learning system built on TensorFlow, specifically tailored for use in the domain of mobile devices. Federated Learning (FL) is a method for training machine learning models across a large corpus of decentralized data residing on edge devices like mobile phones. This paradigm addresses key issues such as data privacy and locality by bringing the "code to the data" instead of centralizing data storage.

System Design Details

The system design emphasizes synchronous training algorithms, particularly the Federated Averaging (FedAvg) algorithm. This choice aligns with the necessity of supporting privacy-enhancing technologies such as differential privacy and Secure Aggregation. The architecture ensures that updates collected from devices are aggregated in a secure manner before being combined in the cloud to update the global model.

Key Features

1. Synchronous Rounds: The system supports synchronous rounds to maintain a consistent and secure method of aggregating model updates. Potential overheads from synchronization are mitigated through various optimization techniques.
2. Device Architecture: The Android-based implementation involves an on-device repository of locally collected data, interfaced through an FL runtime. The FL runtime ensures that model updates and other tasks are performed without adversely affecting user experience.
3. Server Architecture: Based on the Actor Programming Model, the FL server architecture enables scalability across multiple dimensions, such as varying populations and FL task complexities. The system employs actors to handle specific tasks like device selection, aggregation, and coordination, ensuring efficient management of resources.
4. Secure Aggregation: Secure Aggregation is a key protocol that guarantees updates from individual devices remain confidential and secure. This protocol is robust to device dropouts and ensures that aggregated results are only generated when a sufficient number of devices have contributed.
5. Pace Steering: This mechanism regulates device connections to avoid excessive load on the server and to manage the natural diurnal patterns of device availability.
6. Multi-Tenancy and Attestation: The system supports multiple FL populations within a single application and utilizes Android's attestation mechanism to verify the authenticity of participating devices, protecting against compromised inputs.

Practical Applications

The system has been validated in several large-scale applications, such as on-device ranking, content suggestions for on-device keyboards, and next-word prediction. For instance, in deploying next-word prediction models for Google's Gboard, the system processed updates from 1.5 million users over five days, showing effective use of the FL framework.

Implications and Future Directions

The primary implications of this research are twofold. Practically, it enables ML model training in scenarios where data privacy is paramount, and central data aggregation is not feasible. Theoretically, it opens avenues for exploring new algorithms and systems optimizations tailored to decentralized data and synchronous training setups.

The future of federated learning includes addressing potential biases introduced by device eligibility and improving convergence times by optimizing both algorithms and system parameters. Another area of interest is the extension towards generalized federated computation beyond just machine learning tasks, encompassing broader analytics and computational workloads.

Conclusion

The paper provides a detailed and practical guide to building a production-ready federated learning system, demonstrating its feasibility through large-scale deployments. As federated learning continues to evolve, the principles and design choices discussed in this paper will likely form the foundation for future innovations in decentralized machine learning and beyond.