Large language model (LLM) agents have demonstrated significant potential for addressing complex tasks through mechanisms such as chain-of-thought reasoning and tool invocation.However, current frameworks lack explicit supervision during the reasoning process, which may lead to error propagation across reasoning chains and hinder the optimization of intermediate decision-making stages.This paper introduces a novel framework, AgentPro, which enhances LLM agent performance by automated process supervision.AgentPro employs Monte Carlo Tree Search to automatically generate step-level annotations, and develops a process reward model based on these annotations to facilitate fine-grained quality assessment of reasoning.By employing a rejection sampling strategy, the LLM agent dynamically adjusts generation probability distributions to prevent the continuation of erroneous paths, thereby improving reasoning capabilities.Extensive experiments on four datasets indicate that our method significantly outperforms existing agent-based LLM methods (e.g., achieving a 6.32% increase in accuracy on the HotpotQA dataset), underscoring its proficiency in managing intricate reasoning chains.

Traditional federated learning mainly focuses on parallel settings (PFL), which can suffer significant communication and computation costs. In contrast, one-shot and sequential federated learning (SFL) have emerged as innovative paradigms to alleviate these costs. However, the issue of non-IID (Independent and Identically Distributed) data persists as a significant challenge in one-shot and SFL settings, exacerbated by the restricted communication between clients. In this paper, we improve the one-shot sequential federated learning for non-IID data by proposing a local model diversity-enhancing strategy. Specifically, to leverage the potential of local model diversity for improving model performance, we introduce a local model pool for each client that comprises diverse models generated during local training, and propose two distance measurements to further enhance the model diversity and mitigate the effect of non-IID data. Consequently, our proposed framework can improve the global model performance while maintaining low communication costs. Extensive experiments demonstrate that our method exhibits superior performance to existing one-shot PFL methods and achieves better accuracy compared with state-of-the-art one-shot SFL methods on both label-skew and domain-shift tasks (e.g., 6%+ accuracy improvement on the CIFAR-10 dataset). Our code and supplementary are available online: https://github.com/NaiboWang/FedELMY.

As data privacy increasingly becomes a critical societal concern, federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing systems and infrastructures to ease the development of various federated learning algorithms. Similar to deep learning systems such as PyTorch and TensorFlow that boost the development of deep learning, federated learning systems (FLSs) are equivalently important, and face challenges from various aspects such as effectiveness, efficiency, and privacy. In this survey, we conduct a comprehensive review on federated learning systems. To understand the key design system components and guide future research, we introduce the definition of federated learning systems and analyze the system components. Moreover, we provide a thorough categorization for federated learning systems according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture, scale of federation and motivation of federation. The categorization can help the design of federated learning systems as shown in our case studies. By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.

As the population ages, eldercare and healthcare have become major issues in recent years. Crossover healthcare services, instead of individual ones, have become the main form of service provision. In this work, a scenario-based requirement model (SBRM) is proposed for crossover healthcare service. A DSL and a prototype system are designed based on the model as well. Our model defines the requirements as: WHO, in what SCENARIOs, what PROCESSes need to be performed, and what RULEs need to be satisfied. We verify our model in the real case of the MEH (medical, eldercare, healthcare) crossover service. SBRM supports the service better in our cases and shows satisfactory efficiency, effectiveness, and reusability.