Report on Current Developments in Federated Learning
General Direction of the Field
The field of federated learning (FL) is witnessing significant advancements aimed at enhancing efficiency, scalability, and personalization across various domains, including space-air-ground integrated networks, automatic speech recognition (ASR), and large language models (LLMs). The recent developments are characterized by innovative methodologies that address the challenges of data heterogeneity, resource constraints, and communication overheads in distributed learning environments.
Efficient and Scalable Federated Learning: There is a growing emphasis on developing methodologies that enable efficient and scalable FL, particularly for large models and resource-constrained devices. Techniques such as adaptive data offloading, seamless handover, and elastic progressive training are being explored to optimize training time and reduce latency in space-air-ground integrated networks. These approaches leverage edge computing and adaptive network dynamics to enhance the performance of FL in remote and dynamic environments.
Personalized Federated Learning: The need for personalized models that cater to diverse user-specific domains is driving the development of novel FL frameworks. These frameworks aim to address the heterogeneity in data distributions and task types across clients. By incorporating heterogeneous mixture of experts (MoE) and adaptive expert gating, these methods enable more flexible and efficient model personalization, ensuring that each client receives an optimal sub-model tailored to their specific needs.
Memory-Efficient and Parameter-Efficient Techniques: To overcome the limitations imposed by the intensive memory footprint and computational demands of large models, researchers are exploring memory-efficient and parameter-efficient techniques. These include elastic progressive training, parameter-efficient domain adaptation, and adaptive sensitivity-based expert gating. These methods not only reduce the memory usage and computational overhead but also enhance the model performance and training efficiency.
Integration of Advanced Technologies: The integration of advanced technologies such as reconfigurable intelligent surfaces (RIS) and language model-based automation is opening new avenues for efficient and personalized FL. RIS technology is being utilized to mitigate data heterogeneity and enhance communication efficiency, while language model-based automation simplifies the orchestration of FL tasks, making it more accessible and user-friendly.
Noteworthy Papers
Federated Learning of Large ASR Models in the Real World: This paper presents a systematic solution for training full-size ASR models with FL, demonstrating the largest model ever trained with FL and showing significant improvements in training efficiency and model quality.
AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference: Introducing an algorithm-system co-design framework for efficient MoE inference, AdapMoE reduces the average number of activated experts by 25% and achieves a 1.35x speedup without accuracy degradation.
These developments underscore the dynamic and innovative nature of the field, pushing the boundaries of federated learning to new heights.
