An incentive scheme for federated learning in the sky
Wei Yang Bryan Lim, Zehui Xiong, Jiawen Kang, Dusit Tao Niyato, Yang Zhang, Cyril Leung, Chunyan Miao · 2020
The enhanced capabilities of Unmanned Aerial Vehicles have promoted the rapid growth of the Drones-as-a-Service (DaaS) market. To enable privacy-preserving collaborative machine learning among independent DaaS providers, we propose a Federated Learning (FL) based approach. There exists a tradeoff between Service Latency (SL), i.e., the time taken for the training request to be completed, and Age of Information (AoI), i.e., the time elapsed between data aggregation to completion of the FL based training. Given that different training tasks may have varying AoI requirements, we propose a contract-theoretic task-aware incentive scheme that can be calibrated based on the weighted preferences of the model owner. Performance evaluation validates the incentive compatibility and flexibility of our contract design amid information asymmetry.