对抗协作+原型学习！深北莫FedPall开源，联邦学习破局特征漂移，准确率登顶SOTA

Core Viewpoint - The article discusses the FedPall algorithm, which addresses the feature drift problem in federated learning by combining prototype-based adversarial and collaborative learning techniques, achieving state-of-the-art performance across various datasets [2][10]. Methodology - The FedPall framework introduces an adversarial learning mechanism between clients and the server, enhancing feature representation alignment in a unified feature space through collaborative learning [3]. - A hierarchical integration strategy is developed to combine global prototypes with local features, facilitating client-server collaboration [5]. - The server trains a shared global amplifier and utilizes KL divergence to enhance heterogeneous information from different clients, mapping raw data to a unified feature space [5]. - The global classifier is distributed to each client, replacing the local classifiers to improve generalization and mitigate feature drift [6]. Performance Evaluation - FedPall was evaluated on three publicly available feature drift datasets: Digits, Office-10, and PACS, demonstrating superior accuracy compared to classical methods and state-of-the-art baselines [8][10]. - In the Office-10 dataset, FedPall achieved an overall accuracy approximately 3 percentage points higher than the second-best method, ADCOL [10]. - The Digits dataset results showed FedPall outperforming all other models, with an accuracy exceeding the second-best model, FedBN, by about 1.1 percentage points [10]. - FedPall consistently maintained higher average accuracy across all three datasets compared to ADCOL, with improvements ranging from 1.1 to 3 percentage points [12]. Future Directions - The research aims to validate the FedPall framework's generalization capabilities across other data modalities and task types in future studies [13].