| EPSRC Reference: |
EP/X035085/1 |
| Title: |
KUber: Knowledge Delivery System For Machine Learning At Scale |
| Principal Investigator: |
Mohamed Abdelmoniem Sayed, Dr A |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Sch of Electronic Eng & Computer Science |
| Organisation: |
Queen Mary University of London |
| Scheme: |
New Investigator Award |
| Starts: |
01 January 2024 |
Ends: |
31 December 2026 |
Value (£): |
522,781
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Fundamentals of Computing |
| Mathematical Aspects of OR |
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| EPSRC Industrial Sector Classifications: |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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03 Jul 2023
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EPSRC ICT Prioritisation Panel July 2023
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Announced
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Summary on Grant Application Form |
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.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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