Resilient Algorithm

Resilient algorithms aim to create robust computational methods that maintain performance despite various challenges, such as data corruption, adversarial attacks, or unreliable network conditions. Current research focuses on developing resilient algorithms for decentralized optimization and machine learning, employing techniques like cryptographic methods, outlier removal, and consensus protocols across diverse architectures including neural networks and distributed systems. This work is crucial for enabling secure and reliable operation of large-scale machine learning systems and distributed applications in environments prone to failures or malicious actors, improving the trustworthiness and scalability of these technologies.