SPACE: 聯邦學習中的單輪參與者合併貢獻評估

Abstract

聯邦學習（Federated Learning, FL）中參與者貢獻評估近年來受到了廣泛關注，因其在激勵機制、魯棒性增強和客戶選擇等領域的適用性。先前的方法主要依賴於被廣泛采用的夏普利值(Shapley value) 進行參與者評估。然而，儘管使用了基於梯度的模型重構和省略評估不必要的子集等技術，夏普利值的計算仍然需要花費大量的時間。因此，我們提出了一種高效的方法，稱為單輪參與者合併貢獻評估（Single-round Participants Amalgamation for Contribution Evaluation, SPACE）。SPACE 包含兩個創新的模組， 包括了“聯邦知識合併(Federated Knowledge Amalgamation)＂和“基於原型的模型評估(Prototype-based Model Evaluation)＂，通過消除對驗證集大小的依賴，並在單個通信輪內進行參與者評估，從而減少了評估工作的計算量。實驗結果表明，SPACE 在運行時間和皮爾森相關系數（PCC）兩方面優於現有方法。此外，我們在客戶重加權和客戶選擇等應用進行了廣泛實驗，突出了SPACE 的有效性。

The evaluation of participant contribution in federated learning (FL) has recently gained significant attention due to its applicability in various domains, such as incentive mechanisms, robustness enhancement, and client selection. Previous approaches have predominantly relied on the widely adopted Shapley value for participant evaluation. However, the computation of the Shapley value is expensive, despite using techniques like gradient-based model reconstruction and truncating unnecessary evaluations. Therefore, we present an efficient approach called Single-round Participants Amalgamation for Contribution Evaluation (SPACE). SPACE incorporates two novel components, namely Federated Knowledge Amalgamation and Prototype-based Model Evaluation to reduce the evaluation effort by eliminating the dependence on the size of the validation set and enabling participant evaluation within a single communication round. Experimental results demonstrate that SPACE outperforms state-of-the-art methods in terms of both running time and Pearson’s Correlation Coefficient (PCC). Furthermore, extensive experiments conducted on applications, client reweighting, and client selection highlight the effectiveness of SPACE.