  An emerging computational paradigm, named federated edge learning (FEL),
enables intelligent computing at the network edge with the feature of
preserving data privacy for edge devices. Given their constrained resources, it
becomes a great challenge to achieve high execution performance for FEL. Most
of the state-of-the-arts concentrate on enhancing FEL from the perspective of
system operation procedures, taking few precautions during the composition step
of the FEL system. Though a few recent studies recognize the importance of FEL
formation and propose server-centric device selection schemes, the impact of
data sizes is largely overlooked. In this paper, we take advantage of game
theory to depict the decision dilemma among edge devices regarding whether to
participate in FEL or not given their heterogeneous sizes of local datasets.
For realizing both the individual and global optimization, the server is
employed to solve the participation dilemma, which requires accurate
information collection for devices' local datasets. Hence, we utilize mechanism
design to enable truthful information solicitation. With the help of correlated
equilibrium, we derive a decision making strategy for devices from the global
perspective, which can achieve the long-term stability and efficacy of FEL. For
scalability consideration, we optimize the computational complexity of the
basic solution to the polynomial level. Lastly, extensive experiments based on
both real and synthetic data are conducted to evaluate our proposed mechanisms,
with experimental results demonstrating the performance advantages.
