Edge device participation in federating learning (FL) has been typically studied under the lens of device-server communication (e.g., device dropout) and assumes an undying desire from edge devices to participate in FL. As a result, current FL frameworks are flawed when implemented in real-world settings, with many encountering the free-rider problem. In a step to push FL towards realistic settings, we propose RealFM: the first truly federated mechanism which (1) realistically models device utility, (2) incentivizes data contribution and device participation, and (3) provably removes the free-rider phenomena. RealFM does not require data sharing and allows for a non-linear relationship between model accuracy and utility, which improves the utility gained by the server and participating devices compared to non-participating devices as well as devices participating in other FL mechanisms. On real-world data, RealFM improves device and server utility, as well as data contribution, by up to 3 magnitudes and 7x respectively compared to baseline mechanisms.

提出了RealFM这个全球首款真实联邦机制，它通过实际模拟设备效用、激励数据贡献和设备参与以及可证明地消除搭便车现象，进一步推动了联邦学习在真实环境中的发展。在实际数据中，RealFM提高了设备和服务器的效用、数据贡献，与基准机制相比，设备效用提高了3个数量级，服务器效用提高了7倍。

真实FM：激励数据贡献和设备参与的现实机制