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Found 6 papers, 4 papers with code
Workflow Optimization for Parallel Split Learning
We propose a solution method based on the decomposition of the problem by leveraging its inherent symmetry, and a second one that is fully scalable.
MP-SL: Multihop Parallel Split Learning
Moreover, the task of training ML models with a vast number of parameters demands computing and memory resources beyond the capabilities of small devices, such as mobile and Internet of Things (IoT) devices.
Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data
The framework has been used by 3 voluntary users for two weeks, generating a dataset with more than 36K samples and 1331 features.
IPLS : A Framework for Decentralized Federated Learning
The proliferation of resourceful mobile devices that store rich, multidimensional and privacy-sensitive user data motivate the design of federated learning (FL), a machine-learning (ML) paradigm that enables mobile devices to produce an ML model without sharing their data.
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.
