Real-Time Federated Learning at the Wireless Edge via Algorithm-Hardware Co-Design

The past years have witnessed a rapidly growing number of wirelessly-connected devices such as smartphones and Internet-of-Things (IoT) equipment, which generate ever-increasing amounts of data driving key Artificial Intelligence (AI) applications. However, users are increasingly unwilling to allow their private data (such as media, location, or sensor data) to be uploaded to a central location (e.g., cloud datacentre) for training Machine Learning (ML) models, and data-protection laws such as the Data Protection Act 2018 are growing more restrictive towards data usage. Federated Learning (FL) is a game-changing technology conceived to address the growing data privacy concern by moving training from the datacentre to user devices at the network edge, allowing sensitive data to remain on the devices where it was generated. FL has enormous potential for real-world, privacy-sensitive applications such as autonomous driving, diagnostic healthcare, and predictive maintenance.

The operating environment for FL at the edge is extremely challenging for a variety of reasons: 1) the data owned by FL clients is highly heterogeneous (in regard to data distribution, quality, and quantity) and dynamic (data distributions change over time); 2) the hardware devices have diverse computing and communication capabilities with stringent resource constraints (e.g., battery power); and 3) FL clients work under unreliable wireless edge network conditions. Hence, despite FL's huge promise, there are considerable barriers to its wider real-world adoption for mission-critical AI applications that need real-time, on-demand responses, caused by several grand challenges: Challenge 1) lack of FL algorithms delivering consistent performance for dynamic client data, diverse client hardware, and unreliable wireless connections simultaneously; Challenge 2) lack of rigorous theoretical analyses of real-time, real-world FL algorithms; Challenge 3) lack of optimised, energy-efficient, versatile hardware acceleration for real-time FL.

To address these important challenges, this project will create revolutionary algorithm-hardware co-design approaches to make FL a real-time process with unparalleled speed, performance, and energy-efficiency at the wireless edge, capable of meeting the stringent requirements of mission-critical applications. This research will pioneer a set of original methods and innovative technologies including: 1) disruptive lightweight hardware-aware FL algorithms that significantly reduce communication, computing, and energy costs while achieving fast model updates; 2) rigorous mathematical analyses of the proposed algorithms to prove their convergence rates and offer theoretical insights into how they perform under various edge network conditions; 3) an automatic hardware-software co-optimisation framework integrating specialised training-acceleration and power-reduction methods to realise optimised, energy-efficient hardware acceleration; and 4) a unique prototype system that will integrate the designed FL hardware accelerator and real-time FL algorithms and be evaluated in a realistic wireless edge networking testbed.

This project has the potential to transform FL from a lengthy and disjointed process to a continuous, real-time procedure with concurrent model training and deployment. The proposed research will contribute to the UK's digital transformation and green economy by creating ground-breaking technologies for creating innovative AI-empowered products with significantly improved performance and energy-efficiency while complying with strict data-privacy protection.