The Limitations of Federated Learning

In episode 67 of The Gradient Podcast, Daniel Bashir speaks to Daniel Situnayake.

Daniel is head of Machine Learning at Edge Impulse. He is co-author of the O’Reilly books "AI at the Edge" and "TinyML". Previously, he’s worked on the Tensorflow Lite team at Google AI and co-founded Tiny Farms, an insect farming company. Daniel has also lectured in AIDC technologies at Birmingham City University.

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Outline:

* (00:00) Intro

* (1:40) Daniel S Origin Story: computer networking, RFID/barcoding, earlier jobs, Tiny Farms, Tensorflow Lite, writing on TinyML, and Edge Impulse

* (15:30) Edge AI and questions of embodiment/intelligence in AI

* (21:00) The role of hardware, other constraints in edge AI

* (25:00) Definitions of intelligence

* (29:45) What is edge AI?

* (37:30) The spectrum of edge devices

* (43:45) Innovations in edge AI (architecture, frameworks/toolchains, quantization)

* (53:45) Model compression tradeoffs in edge

* (1:00:30) Federated learning and challenges

* (1:09:00) Intro to Edge Impulse

* (1:20:30) Feature engineering for edge systems, fairness considerations

* (1:25:50) Edge AI and axes in AI (large/small, ethereal/embodied)

* (1:37:00) Daniel and Daniel go off the rails on panpsychism

* (1:54:20) Daniel’s advice for aspiring AI practitioners

* (1:57:20) Outro

Links:

* Daniel’s Twitter and blog

* Edge Impulse