PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clients

• URL: http://arxiv.org/abs/2303.10624v1
• Date: Sun, 19 Mar 2023 10:38:29 GMT
• Title: PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clients
• Authors: Manas Wadhwa, Gagan Raj Gupta, Ashutosh Sahu, Rahul Saini, Vidhi Mittal
• Abstract summary: PFSL is a new framework of distributed split learning where a large number of thin clients perform transfer learning in parallel. We implement a lightweight step of personalization of client models to provide high performance for their respective data distributions. Our accuracy far exceeds that of current algorithms SL and is very close to that of centralized learning on several real-life benchmarks.
• Score: 0.5144809478361603
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The traditional framework of federated learning (FL) requires each client to re-train their models in every iteration, making it infeasible for resource-constrained mobile devices to train deep-learning (DL) models. Split learning (SL) provides an alternative by using a centralized server to offload the computation of activations and gradients for a subset of the model but suffers from problems of slow convergence and lower accuracy. In this paper, we implement PFSL, a new framework of distributed split learning where a large number of thin clients perform transfer learning in parallel, starting with a pre-trained DL model without sharing their data or labels with a central server. We implement a lightweight step of personalization of client models to provide high performance for their respective data distributions. Furthermore, we evaluate performance fairness amongst clients under a work fairness constraint for various scenarios of non-i.i.d. data distributions and unequal sample sizes. Our accuracy far exceeds that of current SL algorithms and is very close to that of centralized learning on several real-life benchmarks. It has a very low computation cost compared to FL variants and promises to deliver the full benefits of DL to extremely thin, resource-constrained clients.

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