Federated learning (FL) has been considered as a promising learning framework for future machine learning systems due to its privacy preservation and communication efficiency. In beyond-5G/6G systems, it is likely to have multiple FL groups with different learning purposes. This scenario leads to a question: How does a wireless network support multiple FL groups? As an answer, we first propose to use a cell-free massive multiple-input multiple-output (MIMO) network to guarantee the stable operation of multiple FL processes by letting the iterations of these FL processes be executed together within a large-scale coherence time. We then develop a novel scheme that asynchronously executes the iterations of FL processes under multicasting downlink and conventional uplink transmission protocols. Finally, we propose a simple/low-complexity resource allocation algorithm which optimally chooses the power and computation resources to minimize the execution time of each iteration of each FL process.
翻译:联邦学习(FL)由于其隐私保护和通信效率,被认为是未来机器学习系统的一个有希望的学习框架。在5G/6G系统之外,它可能拥有多个具有不同学习目的的FL组。这个假设导致一个问题:无线网络如何支持多个FL组?作为答案,我们首先提议使用无细胞的大规模多投入多输出产出(MIMO)网络,以保证多个FL进程的稳定运行,方法是让这些FL进程的迭代在大规模一致性时间里一起执行。然后我们开发一个新方案,在多播下行链路和常规上链传输协议中,不同步地执行FL进程的迭代。最后,我们提出一个简单/低兼容的资源分配算法,以最佳方式选择能量和计算资源,以尽量减少每个FL进程的每次循环的执行时间。