Aggregation Strategy

Aggregation strategies, crucial for combining information from multiple sources, are a central focus in diverse fields like federated learning, social choice theory, and graph neural networks. Current research emphasizes developing robust and efficient aggregation methods, exploring architectures like graph neural networks and game-theoretic approaches, and addressing challenges such as data heterogeneity, communication overhead, and vulnerability to adversarial attacks. These advancements improve model accuracy, efficiency, and robustness in various applications, ranging from medical image analysis and drug discovery to decentralized machine learning and collective decision-making.