Boosting Fairness and Robustness in Over-the-Air Federated Learning: Conclusion and References

Table of Links

Abstract and Introduction

Problem Setup

Federated fair over-the-air learning (FedAir) Algorithm

Convergence Properties

Numerical Example

Conclusion and References

VI. CONCLUSION

In this paper, we have introduced the FedFAir algorithm which uses Over-the-Air Computation to carry out efficient decentralized learning while providing fairness and improved performance. We have shown that the FedFAir algorithm converges to an optimal solution of the minimax problem. Furthermore, we have also illustrated our theoretical findings with a numerical example.

Future research will include the development of resilient federated learning algorithms when there are malicious agents in the system.

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