Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data heterogeneity, yet incurs a dilemma in preserving privacy and promoting

A.1 More Information About The Continuous Environment We provide a detailed description of the continuous environments with constrained settings: Let's consider an optimization problem in the form of: minimize α After analyzing Table C.1 and Figure C.1, it is evident that the B2CL, MEICRL, and InfoGAIL-ICRL Although MMICRL-LD shows a notable improvement, its performance remains mediocre in environments involving three types of agents. Table C.2 presents the mean std results of all algorithms in Mujoco. Figure C.2 depicts the distribution of x-coordinate values Half-Cheetah, Blocked Swimmer, and Blocked Walker environments. It demonstrates the algorithm's capacity to infer and restore incorrect We employ "/" to separate the results for various We present the mean std results calculated over 20 runs for each random seed.Method Setting 1 Setting 2 Setting 3 Setting 4 Feasible Cumulative Rewards B2CL 0.24 0 .40 Figure C.1: The feasible cumulative rewards (left two columns of the first three rows and second-to-last row) and constraint violation rate (right two columns of the first three rows and last row). The first row showcases the expert demonstration, followed by the results of B2CL, MEICRL, InfoGAIL-ICRL, MMICRL-LD, and MMICRL algorithms.

We summarize our contributions as follows: 1.

Among them, over-sampling and data augmentation aim to balance the data distribution by oversampling or generating closely related minority classes.