Private Distributed Learning in a Byzantine World

Georgios Damaskinos - Machine Learning Engineer at Facebook- spoke at the ML Fairness Summit in June 2021.

The ever-growing number of edge devices - e.g., smartphones - and the exploding volume of sensitive data they produce, call for distributed machine learning techniques that are privacy-preserving. Given the increasing computing capabilities of modern edge devices, these techniques can be realized by pushing the sensitive-data-dependent tasks of machine learning to the edge devices and thus avoid disclosing sensitive data.

I will present two important challenges in this new computing paradigm along with an overview of our proposed solutions to address them. First, for many applications, such as news recommenders, data needs to be processed fast, before it becomes obsolete. Second, given the large number of uncontrolled edge devices, some of them may undergo arbitrary -Byzantine - failures and deviate from the distributed learning protocol with potentially negative consequences such as learning divergence or even biased predictions.

Key Takeaways:

- Our data is extremely valuable and vulnerable = let's push it to the "Edge"
- Machine Learning at the Edge is possible yet challenging due to A. temporality of the data and B. unreliability of the machines

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