Imperial College London
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ProfessorJulieMcCann

Faculty of Engineering, Department of Computing

Vice-Dean (Research) for the Faculty of Engineering
 
 
 
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Contact

 

+44 (0)20 7594 8375j.mccann Website

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

260ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zhao:2021:10.1109/MNET.111.2100329,
author = {Zhao, C and Sun, X and Yang, S and Ren, X and Zhao, P and McCann, J},
doi = {10.1109/MNET.111.2100329},
journal = {IEEE Network: the magazine of global information exchange},
title = {Exploration across small silos: federated few-shot learning on network edge},
url = {http://dx.doi.org/10.1109/MNET.111.2100329},
volume = {36},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Federated Learning (FL) has been drawing significant attention from both academia and industry working on distributed machine learning. In practice, learning over mutually isolated datasets residing at the network edge, also known as silos, FL clients can suffer from a lack of samples, due to many reasons (e.g., expensive annotation), and this has potentially significant negative impact on FL performance. Few-Shot Learning (FSL) has been considered as a promising solution, but unfortunately cannot be directly applied to practical Cross-Silo Federated Learning (CSFL) systems. In this article, as far as we know, we conduct the first systematic discussion of the specific challenges of FSL in CSFL systems. We extract essential design issues found in Federated Few-Shot Learning (FFSL), and develop a new FFSL method based on Model-Agnostic Meta Learning (MAML). Through experiments using real-world federated datasets, we comprehensively demonstrate our method's advantages over existing FL and FSL methods in different practical CSFL scenarios where hitherto FL and FSL methods failed. We also highlight some promising future research directions.
AU  - Zhao,C
AU  - Sun,X
AU  - Yang,S
AU  - Ren,X
AU  - Zhao,P
AU  - McCann,J
DO  - 10.1109/MNET.111.2100329
PY  - 2021///
SN  - 0890-8044
TI  - Exploration across small silos: federated few-shot learning on network edge
T2  - IEEE Network: the magazine of global information exchange
UR  - http://dx.doi.org/10.1109/MNET.111.2100329
UR  - https://ieeexplore.ieee.org/document/9599589
UR  - http://hdl.handle.net/10044/1/92527
VL  - 36
ER  -
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