Federated Learning (FL), as a rapidly evolving privacy-preserving
collaborative machine learning paradigm, is a promising approach to enable edge
intelligence in the emerging Industrial Metaverse. Even though many successful
use cases have proved the feasibility of FL in theory, in the industrial
practice of Metaverse, the problems of non-independent and identically
distributed (non-i.i.d.) data, learning forgetting caused by streaming
industrial data, and scarce communication bandwidth remain key barriers to
realize practical FL. Facing the above three challenges simultaneously, this
paper presents a high-performance and efficient system named HFEDMS for
incorporating practical FL into Industrial Metaverse. HFEDMS reduces data
heterogeneity through dynamic grouping and training mode conversion (Dynamic
Sequential-to-Parallel Training, STP). Then, it compensates for the forgotten
knowledge by fusing compressed historical data semantics and calibrates
classifier parameters (Semantic Compression and Compensation, SCC). Finally,
the network parameters of the feature extractor and classifier are synchronized
in different frequencies (Layer-wiseAlternative Synchronization Protocol, LASP)
to reduce communication costs. These techniques make FL more adaptable to the
heterogeneous streaming data continuously generated by industrial equipment,
and are also more efficient in communication than traditional methods (e.g.,
Federated Averaging). Extensive experiments have been conducted on the streamed
non-i.i.d. FEMNIST dataset using 368 simulated devices. Numerical results show
that HFEDMS improves the classification accuracy by at least 6.4% compared with
8 benchmarks and saves both the overall runtime and transfer bytes by up to
98%, proving its superiority in precision and efficiency.

本文提出了一个高性能和高效的系统 HFEDMS，用于将实践的 Federated Learning 纳入工业元宇宙，克服了非独立和非同分布数据、学习遗忘和通信成本等挑战，通过动态分组和训练模式转换、语义压缩和补偿以及层间备用同步协议等技术，使得 FL 更加适应于工业设备连续产生的异构流数据，并且比传统方法更加高效和精确。