Over-the-Air Computation is a beyond-5G communication strategy that has
recently been shown to be useful for the decentralized training of machine
learning models due to its efficiency. In this paper, we propose an
Over-the-Air federated learning algorithm that aims to provide fairness and
robustness through minmax optimization. By using the epigraph form of the
problem at hand, we show that the proposed algorithm converges to the optimal
solution of the minmax problem. Moreover, the proposed approach does not
require reconstructing channel coefficients by complex encoding-decoding
schemes as opposed to state-of-the-art approaches. This improves both
efficiency and privacy.

我们提出了一种基于 Over-the-Air 的联邦学习算法，通过最小最大优化以实现公平性和稳健性，无需复杂的编码 - 解码方案即可收敛于最优解，提高了效率和隐私。

提升无线联邦学习中的公平性和鲁棒性

Boosting Fairness and Robustness in Over-the-Air Federated Learning

Cellular vehicle-to-everything (V2X) communication is crucial to support
future diverse vehicular applications. However, for safety-critical
applications, unstable vehicle-to-vehicle (V2V) links and high signalling
overhead of centralized resource allocation approaches become bottlenecks. In
this paper, we investigate a joint optimization problem of transmission mode
selection and resource allocation for cellular V2X communications. In
particular, the problem is formulated as a Markov decision process, and a deep
reinforcement learning (DRL) based decentralized algorithm is proposed to
maximize the sum capacity of vehicle-to-infrastructure users while meeting the
latency and reliability requirements of V2V pairs. Moreover, considering
training limitation of local DRL models, a two-timescale federated DRL
algorithm is developed to help obtain robust model. Wherein, the graph theory
based vehicle clustering algorithm is executed on a large timescale and in turn
the federated learning algorithm is conducted on a small timescale. Simulation
results show that the proposed DRL-based algorithm outperforms other
decentralized baselines, and validate the superiority of the two-timescale
federated DRL algorithm for newly activated V2V pairs.

本文研究了细胞 V2X 通信的传输模式选择和资源分配问题，将其制定为马尔可夫决策过程，提出了基于深度增强学习的去中心化算法，同时开发了基于图论的车辆聚类算法和分布式学习算法。实验结果表明，所提出的算法优于其他分布式基线算法，并验证了两个时间尺度的联邦 DRL 算法在新激活的 V2V 对中的优越性。