The field of distributed computing is moving towards more resilient and efficient algorithms, with a focus on handling faulty networks and dynamic graphs. Recent developments have led to improvements in the computation of tasks in faulty congested cliques, with algorithms that can transform non-faulty models into faulty ones with minimal overhead. Additionally, there have been advancements in finding maximum independent sets in dynamic graphs using unsupervised learning, which have shown to be competitive with state-of-the-art methods and achieve excellent scalability. Moreover, researchers have made progress in resolving long-standing open problems in distributed computing, such as finding maximal independent sets in sublogarithmic rounds. Noteworthy papers in this area include:

• Computing in a Faulty Congested Clique, which presents a method to transform non-faulty algorithms into faulty ones with minimal overhead.
• Breaking Barriers for Distributed MIS by Faster Degree Reduction, which finds an MIS in sublogarithmic rounds for graphs with no cycles of length ≤ 6.