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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 de ...
We introduce a new theoretical model of ad hoc mobile computing in which agents have severely restricted memory, highly unpredictable movement and no initial knowledge of the system. Each agent’s memory can store O(1) bits, plus a unique identifier, and O(1 ...
Asynchronous distributed machine learning solutions have proven very effective so far, but always assuming perfectly functioning workers. In practice, some of the workers can however exhibit Byzantine behavior, caused by hardware failures, software bugs, c ...
This paper establishes the first theorem relating resilience, round complexity and authentication in distributed computing. We give an exact measure of the time complexity of consensus algorithms that tolerate Byzantine failures and arbitrary long periods ...
