The field of distributed optimization and learning is witnessing significant advancements, with a focus on developing innovative algorithms and frameworks that can efficiently handle complex problems. Researchers are exploring new approaches to address challenges such as communication efficiency, delayed feedback, and non-convexity. Notably, the development of flexible and robust algorithms, such as variants of the ALADIN method, is improving the convergence and performance of distributed optimization systems. Additionally, novel evaluation metrics, like distributed forgetting-factor regret, are being proposed to assess the performance of online optimization algorithms. The application of these advancements can be seen in various domains, including sensor allocation, active noise control, and large-scale global optimization. Noteworthy papers include: Distributed Consensus Optimization with Consensus ALADIN, which proposes a novel algorithm for solving distributed consensus optimization problems. Capacity-Constrained Online Learning with Delays, which establishes matching upper and lower bounds on achievable regret under novel capacity constraints. ALADIN-$\beta$: A Distributed Optimization Algorithm for Solving MPCC Problems, which introduces a distributed structure-splitting reformulation to efficiently solve Mathematical Programs with Complementarity Constraints.