Federated meta-learning (FML) has emerged as a promising paradigm to cope with the data limitation and heterogeneity challenges in today's edge learning arena. However, its performance is often limited by slow convergence and corresponding low communication efficiency. In addition, since the available radio spectrum and IoT devices' energy capacity are usually insufficient, it is crucial to control the resource allocation and energy consumption when deploying FML in practical wireless networks. To overcome the challenges, in this paper, we rigorously analyze each device's contribution to the global loss reduction in each round and develop an FML algorithm (called NUFM) with a non-uniform device selection scheme to accelerate the convergence. After that, we formulate a resource allocation problem integrating NUFM in multi-access wireless systems to jointly improve the convergence rate and minimize the wall-clock time along with energy cost. By deconstructing the original problem step by step, we devise a joint device selection and resource allocation strategy to solve the problem with theoretical guarantees. Further, we show that the computational complexity of NUFM can be reduced from $O(d^2)$ to $O(d)$ (with the model dimension $d$) via combining two first-order approximation techniques. Extensive simulation results demonstrate the effectiveness and superiority of the proposed methods in comparison with existing baselines.
翻译:联邦元学习(FML)已成为应对当今边缘学习领域数据限制和差异性挑战的一个很有希望的范例,但在当今边缘学习领域,它的表现往往受到缓慢的趋同和相应的通信效率低的限制;此外,由于现有无线电频谱和IoT装置的能源能力通常不足,因此在实际无线网络中部署FML时,控制资源分配和能源消耗至关重要;为了克服挑战,我们在本文件中严格分析每个设备对减少每轮全球损失的贡献,并发展一个非统一设备选择计划的FML算法(称为NUFM),以加速趋同;之后,我们将NUFM纳入多接入无线系统中的资源分配问题,以共同提高趋同率,并尽可能缩短隔时钟的能源成本;通过逐步解析最初的问题,我们设计了一个联合设备选择和资源分配战略,以理论担保解决问题;此外,我们表明NFMLM的计算复杂性可以从1O(d2)美元降至2美元,以便加速趋同现行最高级技术的基数。