LoLaFL: Low-Latency Federated Learning via Forward-only Propagation

LoLaFL presents a novel approach to federated learning designed to improve low-latency performance, particularly addressing the limitations of traditional methods in emerging 6G mobile networks. This technique centers on forward-only propagation, which streamlines data processing while upholding privacy standards inherent to federated learning frameworks. By focusing on this forward-only mechanism, LoLaFL enhances efficiency, making it well-suited for applications requiring rapid data handling and minimal delay. The approach aligns with ongoing advancements in AI and machine learning, especially within contexts demanding secure and swift distributed learning. Its relevance to 6G networks highlights the increasing need for federated learning solutions that can operate effectively under stringent latency constraints. LoLaFL thus contributes to the evolving landscape of privacy-preserving AI technologies optimized for next-generation communication infrastructures. This development reflects broader trends in federated learning research aiming to balance performance with data confidentiality.