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- Fan, Zhenan, et al.
(författare)
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Improving Fairness for Data Valuation in Horizontal Federated Learning
- 2022
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Ingår i: 38th IEEE International Conference on Data Engineering, ICDE 2022. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2440-2453
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Konferensbidrag (refereegranskat)abstract
- Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federated learning depends largely on the participation of data owners. To sustain and encourage data owners' participation, it is crucial to fairly evaluate the quality of the data provided by the data owners as well as their contribution to the final model and reward them correspondingly. Federated Shapley value, recently proposed by Wang et al. [Federated Learning, 2020], is a measure for data value under the framework of federated learning that satisfies many desired properties for data valuation. However, there are still factors of potential unfairness in the design of federated Shapley value because two data owners with the same local data may not receive the same evaluation. We propose a new measure called completed federated Shapley value to improve the fairness of federated Shapley value. The design depends on completing a matrix consisting of all the possible contributions by different subsets of the data owners. It is shown under mild conditions that this matrix is approximately low-rank by leveraging concepts and tools from optimization. Both theoretical analysis and empirical evaluation verify that the proposed measure does improve fairness in many circumstances.
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| 2. |
- Liu, Changxin, et al.
(författare)
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Private Stochastic Dual Averaging for Decentralized Empirical Risk Minimization
- 2022
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Ingår i: 9th IFAC Conference on Networked Systems NECSYS 2022Zürich, Switzerland, 5–7 July 2022. - : Elsevier BV. ; , s. 43-48
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Konferensbidrag (refereegranskat)abstract
- In this work, we study the decentralized empirical risk minimization problem under the constraint of differential privacy (DP). Based on the algorithmic framework of dual averaging, we develop a novel decentralized stochastic optimization algorithm to solve the problem. The proposed algorithm features the following: i) it perturbs the stochastic subgradient evaluated over individual data samples, with which the information about the dataset can be released in a differentially private manner; ii) it employs hyperparameters that are more aggressive than conventional decentralized dual averaging algorithms to speed up convergence. The upper bound for the utility loss of the proposed algorithm is proven to be smaller than that of existing methods to achieve the same level of DP. As a by-product, when removing the perturbation, the non-private version of the proposed algorithm attains the optimal O(1/t) convergence rate for smooth stochastic optimization. Finally, experimental results are presented to demonstrate the effectiveness of the algorithm.
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