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）能使地理分布的设备根据本地流式数据学习全局共享模型。当前多数在线联邦学习文献仅考虑参与客户端与通信信道处于最佳理想环境。然而，现实应用中这些假设往往难以满足。异步设置能更真实地反映实际环境，例如因计算能力和电池容量限制导致的异构客户端参与模式，以及通信信道或迟滞设备造成的延迟。此外，多数应用场景需考虑能效问题。基于部分共享通信原理，本文提出一种通信高效异步在线联邦学习策略（PAO-Fed）。该方法通过降低参与者的通信开销，使学习任务更易参与且更高效。同时，所提出的聚合机制能应对随机参与模式，处理延迟更新并缓解其对准确性的影响。我们证明了PAO-Fed方法的一阶与二阶收敛性，并推导出其稳态均方偏差表达式。最后，通过综合仿真实验在合成数据集与真实数据集上评估该方法性能。结果表明：在异步设置下，PAO-Fed在实现与在线联邦随机梯度下降法相同收敛特性的同时，可将通信开销降低98%。