FedZKT: Zero-Shot Knowledge Transfer towards Heterogeneous On-Device Models in Federated Learning

• URL: http://arxiv.org/abs/2109.03775v1
• Date: Wed, 8 Sep 2021 16:53:07 GMT
• Title: FedZKT: Zero-Shot Knowledge Transfer towards Heterogeneous On-Device Models in Federated Learning
• Authors: Lan Zhang, Xiaoyong Yuan
• Abstract summary: Federated learning enables distributed devices to learn a shared prediction model without centralizing on-device training data. This paper proposes a new framework supporting federated learning across heterogeneous on-device models via Zero-shot Knowledge Transfer.
• Score: 6.9573683028565885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning enables distributed devices to collaboratively learn a shared prediction model without centralizing on-device training data. Most of the current algorithms require comparable individual efforts to train on-device models with the same structure and size, impeding participation from resource-constrained devices. Given the widespread yet heterogeneous devices nowadays, this paper proposes a new framework supporting federated learning across heterogeneous on-device models via Zero-shot Knowledge Transfer, named by FedZKT. Specifically, FedZKT allows participating devices to independently determine their on-device models. To transfer knowledge across on-device models, FedZKT develops a zero-shot distillation approach contrary to certain prior research based on a public dataset or a pre-trained data generator. To utmostly reduce on-device workload, the resource-intensive distillation task is assigned to the server, which constructs a generator to adversarially train with the ensemble of the received heterogeneous on-device models. The distilled central knowledge will then be sent back in the form of the corresponding on-device model parameters, which can be easily absorbed at the device side. Experimental studies demonstrate the effectiveness and the robustness of FedZKT towards heterogeneous on-device models and challenging federated learning scenarios, such as non-iid data distribution and straggler effects.

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