Online federated learning (FL) enables geographically distributed devices to learn a global shared model from locally available streaming data. Most online FL literature considers a best-case scenario regarding the participating clients and the communication channels. However, these assumptions are often not met in real-world applications. Asynchronous settings can reflect a more realistic environment, such as heterogeneous client participation due to available computational power and battery constraints, as well as delays caused by communication channels or straggler devices. Further, in most applications, energy efficiency must be taken into consideration. Using the principles of partial-sharing-based communications, we propose a communication-efficient asynchronous online federated learning (PAO-Fed) strategy. By reducing the communication overhead of the participants, the proposed method renders participation in the learning task more accessible and efficient. In addition, the proposed aggregation mechanism accounts for random participation, handles delayed updates and mitigates their effect on accuracy. We prove the first and second-order convergence of the proposed PAO-Fed method and obtain an expression for its steady-state mean square deviation. Finally, we conduct comprehensive simulations to study the performance of the proposed method on both synthetic and real-life datasets. The simulations reveal that in asynchronous settings, the proposed PAO-Fed is able to achieve the same convergence properties as that of the online federated stochastic gradient while reducing the communication overhead by 98 percent.

翻译：在线联邦学习（FL）使地理上分布的设备能够从本地的流式数据中学习全局共享模型。大多数在线联邦学习文献都假设参与客户端和通信信道处于最佳场景。然而，这些假设在实际应用中往往不成立。异步设置可以反映更真实的环境，例如由于可用计算能力和电池限制导致的异构客户端参与，以及由通信信道或落后设备引起的延迟。此外，大多数应用还需要考虑能量效率。利用基于部分共享的通信原则，我们提出了一种通信高效的异步在线联邦学习（PAO-Fed）策略。通过降低参与者的通信开销，该方法使参与学习任务变得更加便捷和高效。此外，所提出的聚合机制考虑了随机参与，处理了延迟更新并减轻了其对准确性的影响。我们证明了所提出的PAO-Fed方法的一阶和二阶收敛性，并得到了其稳态均方偏差的表达式。最后，我们进行了全面的仿真实验，在合成数据集和真实数据集上研究了所提方法的性能。仿真结果表明，在异步设置中，所提出的PAO-Fed能够达到与在线联邦随机梯度下降相同的收敛特性，同时将通信开销降低了98%。