The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as privacy, heterogeneity and resource efficiency. Federated learning is an effective way to enable AI over massive distributed nodes with security. However, conventional works mostly focus on learning a single global model for a unique task across the network, and are generally less competent to handle multi-task learning (MTL) scenarios with stragglers at the expense of acceptable computation and communication cost. Meanwhile, it is challenging to ensure the privacy while maintain a coupled multi-task learning across multiple base stations (BSs) and terminals. In this paper, inspired by the natural cloud-BS-terminal hierarchy of cellular works, we provide a viable resource-aware hierarchical federated MTL (RHFedMTL) solution to meet the heterogeneity of tasks, by solving different tasks within the BSs and aggregating the multi-task result in the cloud without compromising the privacy. Specifically, a primal-dual method has been leveraged to effectively transform the coupled MTL into some local optimization sub-problems within BSs. Furthermore, compared with existing methods to reduce resource cost by simply changing the aggregation frequency, we dive into the intricate relationship between resource consumption and learning accuracy, and develop a resource-aware learning strategy for local terminals and BSs to meet the resource budget. Extensive simulation results demonstrate the effectiveness and superiority of RHFedMTL in terms of improving the learning accuracy and boosting the convergence rate.

翻译：人工智能在蜂窝网络物联网等大规模应用中的快速发展引发了隐私、异构性和资源效率等技术挑战。联邦学习是一种能够在保障安全的前提下实现大规模分布式节点人工智能的有效方法。然而，现有研究主要聚焦于在网络中学习单一任务的全局模型，难以有效处理存在落后节点的多任务学习场景，且通常需要付出可接受的计算与通信代价。同时，如何在多个基站和终端之间保持耦合多任务学习的同时确保隐私性仍是一个挑战。本文受蜂窝网络天然具有的云-基站-终端层次结构启发，提出了一种可行的资源感知分层联邦多任务学习（RHFedMTL）方案，通过在各基站内解决不同任务并在云端聚合多任务结果，在不牺牲隐私的前提下应对任务的异构性。具体而言，采用原始-对偶方法将耦合的多任务学习有效转化为基站内的局部优化子问题。此外，与现有通过简单调整聚合频率来降低资源消耗的方法不同，我们深入研究了资源消耗与学习精度之间的复杂关系，并为本地终端和基站开发了资源感知的学习策略以满足资源预算要求。大量仿真结果证明了RHFedMTL在提高学习精度和加速收敛速度方面的有效性与优越性。