Averaging Algorithm

Averaging algorithms are fundamental computational tools aiming to efficiently compute the average of values distributed across multiple agents or data sources, often in decentralized or distributed systems. Current research focuses on improving robustness against failures or malicious actors, accelerating convergence in asynchronous or heterogeneous settings (e.g., federated learning), and developing adaptive averaging schemes that optimize for various performance metrics like accuracy and generalization. These advancements are crucial for enhancing the scalability and reliability of machine learning, distributed optimization, and other applications requiring efficient consensus-building across networks.