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- W3136133348 abstract "Decentralized machine learning has been playing an essential role in improving training efficiency. It has been applied in many real-world scenarios, such as edge computing and IoT. However, in fact, networks are dynamic, and there is a risk of information leaking during the communication process. To address this problem, we propose a decentralized parallel stochastic gradient descent algorithm (D-(DP)2SGD) with differential privacy in dynamic networks. With rigorous analysis, we show that D-(DP)2SGD converges with a rate of <math xmlns=http://www.w3.org/1998/Math/MathML id=M1> <mi>O</mi> <mfenced open=( close=)> <mrow> <mn>1</mn> <mo>/</mo> <msqrt> <mrow> <mi>K</mi> <mi>n</mi> </mrow> </msqrt> </mrow> </mfenced> </math> while satisfying <math xmlns=http://www.w3.org/1998/Math/MathML id=M2> <mi>ε</mi> </math> -DP, which achieves almost the same convergence rate as previous works without privacy concern. To the best of our knowledge, our algorithm is the first known decentralized parallel SGD algorithm that can implement in dynamic networks and take privacy-preserving into consideration." @default.
- W3136133348 created "2021-03-29" @default.
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- W3136133348 date "2021-03-23" @default.
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- W3136133348 title "D-(DP)2SGD: Decentralized Parallel SGD with Differential Privacy in Dynamic Networks" @default.
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- W3136133348 doi "https://doi.org/10.1155/2021/6679453" @default.
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