Prof. Jon Crowcroft
University of Cambridge, UK
Extreme federation of processing data in mobile networked sensor systems is becoming mainstream as an approach. It reduces load on the uplinks, it saves energy and potentially provides better privacy for personal data.
There are a variety of techniques ranging from simple aggregation, compressive sensing, and edge-machine learning, where models are locally acquired, and model parameters are distributed, so nodes can further refine their models.
There are a number of challenges to scaling such approaches. Firstly to scale federated learning to billions of nodes needs some way to scale even just sharing model parameters - I will discuss some of these, including sampling of model parameters (thinning, probabilistic update) and self organising hierarchies of aggregation (model parameter servers). For some Machine Learning algorithms, there may be updates from the federated model back to nodes to adjust their learning (e.g. regret) as well.
Some schemes may require synchronisation of learning steps. All these need to scale out, and techniques form data centers may, surprisingly be applicable, even though we are often in a much less rich networking environment, even without full connectivity or symmetric bandwidth or reachability.
Federation alone is not a complete solution to privacy, and there are some further techniques may be needed to reduce the loss of confidentiality - e.g. differential privacy is useful, but also more fundamental approaches such as secure multi-party computation, in extreme cases.
Secondly, there is the problem of bad actors injecting false data (pollution). Then there is the omnipresent presence of possible DDoS attacks.
Thirdly, a federated model may present some challenges to model explain-ability or interpret-ability. There are interesting trade-offs between these requirements, and those of privacy.
Jon Crowcroft has been the Marconi Professor of Communications Systems in the Computer Laboratory since October 2001. He has worked in the area of Internet support for multimedia communications for over 30 years. Three main topics of interest have been scalable multicast routing, practical approaches to traffic management, and the design of deployable end-to-end protocols. Current active research areas are Opportunistic Communications, Social Networks, Privacy Preserving Analytics, and techniques and algorithms to scale infrastructure-free mobile systems. He leans towards a "build and learn" paradigm for research. From 2016-2018, he was Programm Chair at the Turing, the UK's national Data Science and AI Institute, and is now researcher-at-large there. He graduated in Physics from Trinity College, University of Cambridge in 1979, gained an MSc in Computing in 1981 and PhD in 1993, both from UCL. He is a Fellow the Royal Society, a Fellow of the ACM, a Fellow of the British Computer Society, a Fellow of the IET and the Royal Academy of Engineering and a Fellow of the IEEE.
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