Abstract
Autonomous agents, including unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), unmanned surface vessels (USVs), and unmanned underwater vehicles (UUVs), are widely applied across diverse fields, such as environmental monitoring, logistics, exploration, and military applications, due to their ability to operate autonomously in complex environments. As tasks grow in complexity, there is a shift from individual autonomous agents to collaborative autonomous agent swarms (AASs). These swarms, connected through wireless links, leverage collective intelligence to perform more sophisticated tasks than individual autonomous agents can achieve alone. Deploying AASs introduces challenges in coordination, resource utilization, and adaptability, which traditional model-based methods struggle to address. Recent advancements in machine learning (ML), particularly distributed machine learning (DML) techniques such as federated learning (FL) and multi-agent reinforcement learning (MARL), offer promising solutions. These techniques enable autonomous agents to learn collaboratively without centralizing data, thereby preserving efficiency while adapting to dynamic environments. However, applying DML in AASs presents unique challenges due to autonomous agents’ dynamic movement, unreliable communication links, harsh operating conditions, heterogeneity, and resource constraints. Therefore, this survey provides a comprehensive review of integrating DML into AASs. Specifically, we analyze four representative types of autonomous agents in a unified perspective examining their fundamental characteristics, communication models, and dynamic behaviors. Then, we discuss the limitations of basic ML models in AASs, highlighting the need for DML and the key requirements and metrics for its successful implementation. Furthermore, we explore recent advancements in FL and MARL as applied to AASs, highlighting use cases and key techniques. By identifying current technological progress and gaps in the literature, this survey offers valuable insights for researchers and practitioners and outlines potential directions for future research to enhance the capabilities of AASs through DML.