Towards Fairness and Green Semantic Communication System: An Anti-Discrimination Federated Learning Approach

Towards addressing emerging energy challenges posed by unfair heterogeneous Semantic Communication (SC) codec updates within future wireless networks, this paper presents a novel Anti-discrimination Federated learning (AdFed) approach. Inspired by the economics of discrimination, unique fairness-associated energy concerns in SC systems are formulated as model discrimination challenges, with the SC-deployed wireless network conceptualized as an anti-discrimination labor market. A novel 'affirmative action' strategy, based on training epochs, is proposed and adopted according to historical training unfairness results. To address the reverse discrimination issues in 'affirmative action' caused by quota fairness impacting training energy cost, we formulate this problem as a coupled integer non-linear programming problem. Moreover, a new quota trade-off mechanism based on the Rubinstein bargaining game is also designed. Simulation results verify that AdFed outperforms SC training baselines, effectively addressing the unique model discrimination challenges of SC codec model heterogeneity updating. The efficacy of the game theoretical trade-off mechanism is demonstrated in achieving optimal outcomes.