Prototyping Opportunistic Learning in Resource Constrained Mobile Devices
Haoxiang Yu, Hsiao-Yuan Chen, Sangsu Lee, Xi Zheng, Christine Julien · 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) · 2022
With the increasing capabilities of pervasive computing devices, training machine learning models on-device has become feasible. At the same time, demands for increased user privacy and reduced communication overhead have brought decentralized machine learning to the forefront. In these paradigms, individual devices collaborate opportunistically to train models using locally available data. In this paper, we examine the practical feasibility of such opportunistic learning. In a basic opportunistic learning approach, when a device (the learner) encounters another device (the neighbor), it can request the neighbor to perform training on the learner’s behalf using the neighbor’s own local data. To realize an opportunistic learning in the real world, one must solve two challenges: (1) leveraging device-to-device communication to discover neighbors and exchange models and (2) training models on the device subject to resource and latency constraints. In this paper, we examine the feasibility of implementing opportunistic learning to learn a convolutional neural network (CNN) model for an image classification task using a small network testbed of diverse iOS devices. We demonstrate success in implementing a completely decentralized approach and characterize the challenges and opportunities that lie ahead.