1 code implementation • 9 Nov 2020 • Kamalesh Palanisamy, Vivek Khimani, Moin Hussain Moti, Dimitris Chatzopoulos
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.
no code implementations • 10 Jun 2019 • Moin Hussain Moti, Dimitris Chatzopoulos, Pan Hui, Sujit Gujar
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.