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

RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation