The field of federated learning is moving towards addressing the challenges of decentralization, asynchronous communication, and Byzantine attacks. Researchers are exploring innovative solutions to provide model personalization, resiliency, and fault tolerance in federated learning settings. Notably, the development of online decentralized federated multi-task learning algorithms and asynchronous decentralized FL approaches with adaptive termination detection are advancing the field. Additionally, novel techniques such as delayed momentum aggregation and semi-decentralized client selection methods are being proposed to improve the performance and robustness of federated learning systems. Some noteworthy papers in this regard include: Online Decentralized Federated Multi-task Learning With Trustworthiness in Cyber-Physical Systems, which proposes an algorithm leveraging cyber-physical properties to assign trust probabilities to local models. Fault-Tolerant Decentralized Distributed Asynchronous Federated Learning with Adaptive Termination Detection, which develops an asynchronous FL framework with fault tolerance mechanisms and novel techniques for autonomous termination detection. Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation, which introduces a novel principle of delayed momentum aggregation for Byzantine-robust FL with partial participation. Semi-decentralized Federated Time Series Prediction with Client Availability Budgets, which proposes a novel client selection method applying probabilistic rankings of available clients. Distributed Download from an External Data Source in Asynchronous Faulty Settings, which presents query-optimal deterministic solutions for the Download problem in asynchronous communication networks. A Simple and Robust Protocol for Distributed Counting, which presents a new robust protocol for distributed counting that achieves optimal communication complexity.