The field of distributed systems is moving towards developing more secure and scalable solutions. Researchers are exploring new approaches to enable secure inference for graph neural networks, such as using distributed secure multi-party computation techniques. There is also a growing interest in developing algorithms for reliable broadcast and consensus that require weaker assumptions than previous solutions, allowing for more flexible and robust distributed systems. Additionally, the use of randomness and fault-tolerant components, such as secret random oracles, is being investigated to mitigate ordering attacks and ensure equal opportunity in state machine replication. Furthermore, researchers are working on improving the scalability of blockchain systems by relaxing the strict total order requirement among transactions and using setchain algorithms. Noteworthy papers include: CryptGNN, which presents a secure and effective inference solution for third-party graph neural network models in the cloud. Setchain Algorithms for Blockchain Scalability, which proposes and evaluates three distinct Setchain algorithms that leverage an underlying block-based ledger to increase blockchain scalability. Ordered Consensus with Equal Opportunity, which extends ordered consensus to support equal opportunity, a concrete notion of fairness, and introduces the secret random oracle to generate randomness in a fault-tolerant manner.