Imperial College London
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DrStefanVlaski

Faculty of Engineering, Department of Electrical and Electronic Engineering

Lecturer
 
 
 
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s.vlaski

 
 
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Location

 

810Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Kayaalp:2022:10.1109/OJSP.2021.3140000,
author = {Kayaalp, M and Vlaski, S and Sayed, A},
doi = {10.1109/OJSP.2021.3140000},
journal = {IEEE Open Journal of Signal Processing},
pages = {71--93},
title = {Dif-MAML: Decentralized multi-agent meta-learning},
url = {http://dx.doi.org/10.1109/OJSP.2021.3140000},
volume = {3},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The objective of meta-learning is to exploit knowledge obtained from observed tasks to improve adaptation to unseen tasks. Meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of data per task. Given the amount of resources that are needed, it is generally difficult to expect the tasks, their respective data, and the necessary computational capacity to be available at a single central location. It is more natural to encounter situations where these resources are spread across several agents connected by some graph topology. The formalism of meta-learning is actually well-suited for this decentralized setting, where the learner benefits from information and computational power spread across the agents. Motivated by this observation, we propose a cooperative fully-decentralized multi-agent meta-learning algorithm, referred to as Diffusion-based MAML or Dif-MAML. Decentralized optimization algorithms are superior to centralized implementations in terms of scalability, robustness, avoidance of communication bottlenecks, and privacy guarantees. The work provides a detailed theoretical analysis to show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the aggregate MAML objective even in non-convex environments. Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting.
AU  - Kayaalp,M
AU  - Vlaski,S
AU  - Sayed,A
DO  - 10.1109/OJSP.2021.3140000
EP  - 93
PY  - 2022///
SN  - 2644-1322
SP  - 71
TI  - Dif-MAML: Decentralized multi-agent meta-learning
T2  - IEEE Open Journal of Signal Processing
UR  - http://dx.doi.org/10.1109/OJSP.2021.3140000
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000752007300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://ieeexplore.ieee.org/document/9669064
UR  - http://hdl.handle.net/10044/1/103743
VL  - 3
ER  -
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