Kavli Affiliate: Feng Wang
| First 5 Authors: Feng Wang, M. Cenk Gursoy, Senem Velipasalar, ,
| Summary:
In this paper, we propose feature-based federated transfer learning as a
novel approach to improve communication efficiency by reducing the uplink
payload by multiple orders of magnitude compared to that of existing approaches
in federated learning and federated transfer learning. Specifically, in the
proposed feature-based federated learning, we design the extracted features and
outputs to be uploaded instead of parameter updates. For this distributed
learning model, we determine the required payload and provide comparisons with
the existing schemes. Subsequently, we analyze the robustness of feature-based
federated transfer learning against packet loss, data insufficiency, and
quantization. Finally, we address privacy considerations by defining and
analyzing label privacy leakage and feature privacy leakage, and investigating
mitigating approaches. For all aforementioned analyses, we evaluate the
performance of the proposed learning scheme via experiments on an image
classification task and a natural language processing task to demonstrate its
effectiveness.
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