We propose FCGrad, a conflict-aware gradient adjustment method that improves social welfare while ensuring fairness in multi-agent mixed-motive settings by resolving conflicts between individual and collective objectives.

We provided individual regret bounds for cooperative stochastic multi-armed bandits over communication graphs, independent of graph diameter, and also analyzed trade-offs with message size and communication rounds.