Achieving Privacy-Preserving and Communication-Efficient Federated Learning in Internet of Unmanned Agents

With the rapid advancement of ubiquitous connectivity, the Internet of Unmanned Agents (IUA) has emerged as a promising paradigm for distributed intelligent perception and decision-making. In such systems, numerous unmanned agents collaboratively collect environmental data to support coordinated tasks. To preserve data privacy, Federated Learning (FL), as a decentralized machine learning framework, enables collaborative training of a global model across agents without directly exchanging raw data. However, FL faces several critical challenges in IUA environments, including high communication overhead under constrained wireless bandwidth, potential privacy leakage from shared model parameters, and agent dropouts caused by unstable connectivity. To address these challenges, we propose PPE-FL, a privacy-preserving and communication-efficient federated learning scheme designed for IUA scenarios. PPE-FL replaces conventional high-dimensional gradient uploads with lightweight ranking-based votes, substantially reducing communication overhead. Then, we design an obfuscation mechanism to protect the privacy of locally generated ranking-based votes, safeguarding both raw data and intermediate parameters. Furthermore, PPE-FL supports mask reconstruction through partial interactions among online unmanned agents, enabling robust aggregation under dynamic network conditions. Experimental results demonstrate that the proposed PPE-FL reduces communication costs by 89.5% compared to VCD-FL and by 87.7% compared to PPML, while maintaining model accuracy and privacy protection.