AI/ML systems are becoming an integral part of user products and applications as well as the main revenue driver for most organizations. This resulted in shifting the focus toward the Edge AI paradigm as edge devices possess the data necessary for training the models. Main Edge AI approaches either coordinate the training rounds and exchange model updates via a central server (i.e., Federated Learning), split the model training task between edge devices and a server (i.e., split Learning), or coordinate the model exchange among the edge devices via gossip protocols (i.e., decentralized training). Due to the highly heterogeneous learners, configurations, environment as well as significant synchronization challenges, these approaches are ill-suited for distributed edge learning at scale. They fail to scale with a large number of learners and produce models with low qualities at prolonged training times. It is imperative for modern applications to rely on a system providing timely and accurate models. This project addresses this gap by proposing a ground-up transformation to decentralized learning methods. Similar to Uber's delivery services, the goal of KUber is to build a novel distributed architecture to facilitate the exchange and delivery of acquired knowledge among the learning entities. In particular, we seize an opportunity to decouple the training task of a common model from the sharing task of learned knowledge. This is made possible by the advances in the AI/ML accelerators embedded in edge devices and the high-throughput and low-latency 5G/6G technologies. KUber will revolutionize the use of AI/ML methods in daily-life applications and open the door for flexible, scalable, and efficient collaborative learning between users, organizations, and governments.

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.

Digital technologies have a transformative impact in the economy and wider society. New innovations in Information Communication Technology (ICT) such as the next generation '5G' internet, automation and robotics, and big data and Artificial Intelligence (AI) have the potential to make a profound societal impact and the pace of development is staggering. The same technologies can though have a negative impact on society, including significantly increasing the carbon emissions related to ICT and thus creating damaging impacts on our environment. Managing this duality between ICT's benefits and risks must be at the heart of future ICT design and innovation - ensuring ICT can continue to bring value to our society and the economy, while keeping ICT innovations from exceeding planetary boundaries. However, there is currently scarce consideration of systemic impacts within ICT innovation, and design processes today lack the information and tools required to embed environmental sustainability into ICT. This project, PARIS-DE, will ensure that the carbon emissions associated with the ICT sector are aligned with the Paris agreement: limiting temperature increases to 1.5 degrees Celsius. To do this, the PARIS-DE project will develop a digital sustainability framework that systemically considers ICT's impacts and ensures Paris-compliant design through two key concepts: i) an evidence base around carbon emissions in the digital economy, and ii) a responsible innovation approach that targets environmental sustainability, yet maintains key aspects of ICT design that enable societal thriving. Using a range of disciplinary perspectives including computer science, human-centred design, philosophy and ethics and environmental economics, PARIS-DE will develop digital tools that support ICT development within planetary boundaries, and will create, demonstrate and evaluate the digital sustainability framework through three case studies: 1) big data and AI, 2) autonomous systems, and 3) video streaming. These case studies, taken as representative of the digital economy, will allow for an evaluation of different underlying technologies that threaten rising emissions. The case studies will also involve working closely with key stakeholders in ICT innovation (e.g. designers and developers in the ICT sector), ensuring the framework is comprehensive and effective. PARIS-DE will ultimately allow the ICT sector to innovate technology more sustainably and in-line with climate change mitigation targets.