Federated learning paradigm to utilize datasets across multiple data
providers. In FL, cross-silo data providers often hesitate to share their
high-quality dataset unless their data value can be fairly assessed. Shapley
value (SV) has been advocated as the standard metric for data valuation in FL
due to its desirable properties. Tuttavia, the computational overhead of SV is
prohibitive in practice, as it inherently requires training and evaluating an
FL model across an exponential number of dataset combinations. Inoltre,
existing solutions fail to achieve high accuracy and efficiency, making
practical use of SV still out of reach, because they ignore choosing suitable
computation scheme for approximation framework and overlook the property of
utility function in FL. We first propose a unified stratified-sampling
framework for two widely-used schemes. Then, we analyze and choose the more
promising scheme under the FL linear regression assumption. Dopo di che, Noi
identify a phenomenon termed key combinations, where only limited dataset
combinations have a high-impact on final data value. Building on these
insights, we propose a practical approximation algorithm, IPSS, which
strategically selects high-impact dataset combinations rather than evaluating
all possible combinations, thus substantially reducing time cost with minor
approximation error. Inoltre, we conduct extensive evaluations on the FL
benchmark datasets to demonstrate that our proposed algorithm outperforms a
series of representative baselines in terms of efficiency and effectiveness.

Questo articolo esplora i giri e le loro implicazioni.

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