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Found 2 papers, 1 papers with code
SplitEasy: A Practical Approach for Training ML models on Mobile Devices
In this work, we highlight the theoretical and technical challenges that need to be resolved to develop a functional framework that trains ML models in mobile devices without transferring raw data to a server.
FaRM: Fair Reward Mechanism for Information Aggregation in Spontaneous Localized Settings (Extended Version)
FaRM uses \textit{(i)} a \emph{report strength score} to remove the risk of random pairing with dishonest reporters, \textit{(ii)} a \emph{consistency score} to measure an agent's history of accurate reports and distinguish valuable reports, \textit{(iii)} a \emph{reliability score} to estimate the probability of an agent to collude with nearby agents and prevents agents from getting swayed, and \textit{(iv)} a \emph{location robustness score} to filter agents who try to participate without being present in the considered setting.
