In Federated Learning (FL), data communication among clients is denied. However, it is difficult to learn from the decentralized client data, which is under-sampled, especially for segmentation tasks that need to extract enough contextual semantic information. Existing FL studies always average client models to one global model in segmentation tasks while neglecting the diverse knowledge extracted by the models. To maintain and utilize the diverse knowledge, we propose a novel training paradigm called Federated Learning with Z-average and Cross-teaching (FedZaCt) to deal with segmentation tasks. From the model parameters’ aspect, the Z-average method constructs individual client models, which maintain diverse knowledge from multiple client data. From the model distillation aspect, the Cross-teaching method transfers the other client models’ knowledge to supervise the local client model. In particular, FedZaCt does not have the global model during the training process. After training, all client models are aggregated into the global model by averaging all client model parameters. The proposed methods are applied to two medical image segmentation datasets including our private aortic dataset and a public HAM10000 dataset. Experimental results demonstrate that our methods can achieve higher Intersection over Union values and Dice scores.

With the widespread application of blockchain technology across various industries, detecting and analyzing performance bottlenecks is crucial for evaluating and optimizing blockchain system performance. However, current research needs general performance metrics for detecting and analyzing bottlenecks. Only some studies focus on this aspect within blockchain systems. To address this, this paper first proposes 18 fine-grained performance metrics to evaluate performance across various layers of blockchain systems comprehensively. Subsequently, we introduce a generalized loosely coupled performance measurement framework to capture these metrics and construct the causal relationship between them, i.e., the mesoscopic performance structure. This approach allows for the detection and analysis of performance bottlenecks. Finally, numerous experimental results demonstrate that the causality between the relevant performance metrics disappears when the system reaches a performance bottleneck. Additionally, the framework has a performance impact of less than 15% on ChainMaker.

Sha Jiu, Yu Weng, Mengxiao Zhu, Chong Feng, Zheng Liu, Jialedongzhu. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.