The field of federated learning and multi-agent systems is witnessing significant developments, with a focus on creating sustainable and inclusive frameworks for urban agriculture, modeling strategic systems, and ensuring safe navigation in complex environments. Researchers are exploring innovative approaches to federated learning, such as dashboarding tools and analytical frameworks to identify behavioral incentives and collective performance loss. Additionally, there is a growing interest in multi-agent systems, with studies on target enclosing control, motion planning, and pursuit games. These advancements have the potential to improve urban food security, enhance cooperation in federated learning, and enable safe navigation in hazardous environments. Noteworthy papers include:

• A study on gaming and cooperation in federated learning, which introduces an analytical framework to quantify behavioral incentives and collective performance loss.
• A paper on target enclosing control for nonholonomic multi-agent systems, which proposes a novel control scheme to handle distance constraints and collision avoidance.
• A research on safe multi-agent motion planning, which presents a fully data-driven framework for homogeneous linear multi-agent systems operating in shared workspaces.