Federated Split Learning with Only Positive Labels for resource-constrained IoT environment

• URL: http://arxiv.org/abs/2307.13266v1
• Date: Tue, 25 Jul 2023 05:33:06 GMT
• Title: Federated Split Learning with Only Positive Labels for resource-constrained IoT environment
• Authors: Praveen Joshi, Chandra Thapa, Mohammed Hasanuzzaman, Ted Scully, and Haithem Afli
• Abstract summary: Distributed collaborative machine learning (DCML) is a promising method in the Internet of Things (IoT) domain for training deep learning models. We propose splitfed learning, known as splitfed learning (SFL), is the most suitable for efficient training and testing when devices have limited computational capabilities. We show that SFPL outperforms SFL when resource-constrained IoT devices have only positive labeled data.
• Score: 4.055662817794178
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Distributed collaborative machine learning (DCML) is a promising method in the Internet of Things (IoT) domain for training deep learning models, as data is distributed across multiple devices. A key advantage of this approach is that it improves data privacy by removing the necessity for the centralized aggregation of raw data but also empowers IoT devices with low computational power. Among various techniques in a DCML framework, federated split learning, known as splitfed learning (SFL), is the most suitable for efficient training and testing when devices have limited computational capabilities. Nevertheless, when resource-constrained IoT devices have only positive labeled data, multiclass classification deep learning models in SFL fail to converge or provide suboptimal results. To overcome these challenges, we propose splitfed learning with positive labels (SFPL). SFPL applies a random shuffling function to the smashed data received from clients before supplying it to the server for model training. Additionally, SFPL incorporates the local batch normalization for the client-side model portion during the inference phase. Our results demonstrate that SFPL outperforms SFL: (i) by factors of 51.54 and 32.57 for ResNet-56 and ResNet-32, respectively, with the CIFAR-100 dataset, and (ii) by factors of 9.23 and 8.52 for ResNet-32 and ResNet-8, respectively, with CIFAR-10 dataset. Overall, this investigation underscores the efficacy of the proposed SFPL framework in DCML.

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