Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

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This paper is a tutorial on ongoing work in physical-disorder-based security, security analysis, and implementation choices.

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As semiconductor manufacturing requires greater capital investments, the use of contract foundries has grown dramatically, increasing exposure to mask theft and unauthorized excess production. While only recently studied, IC piracy has now become a major challenge for the electronics and defense industries [6].We propose a novel comprehensive technique to end piracy of integrated circuits (EPIC). It requires that every chip be activated with an external key, which can only be generated by the holder of IP rights, and cannot be duplicated. EPIC is based on (i) automatically-generated chip IDs, (ii) a novel combinational locking algorithm, and (iii) innovative use of public-key cryptography. Our evaluation suggests that the overhead of EPIC on circuit delay and power is negligible, and the standard flows for verification