Retrieval-Augmented Generation (RAG) aims to generate more reliable and accurate responses, by augmenting large language models(LLMs) with the external vast and dynamic knowledge. Most previous work focuses on using RAG for single-round question answering, while how to adapt RAG to the complex conversational setting wherein the question is interdependent on the preceding context is not well studied. In this paper, we propose a conversation-level RAG (ConvRAG) approach, which incorporates fine-grained retrieval augmentation and self-check for conversational question answering (CQA). In particular, our approach consists of three components, namely conversational question refiner, fine-grained retriever and self-check based response generator, which work collaboratively for question understanding and relevant information acquisition in conversational settings. Extensive experiments demonstrate the great advantages of our approach over the state-of-the-art baselines. Moreover, we also release a Chinese CQA dataset with new features including reformulated question, extracted keyword, retrieved paragraphs and their helpfulness, which facilitates further researches in RAG enhanced CQA.

Federated Learning (FL) has drawn much attention for distributed system over the Internet of Things (IoT), since it enables collaborative machine learning on heterogeneous devices while resolves concerns about privacy leakage. Due to the catastrophic forgetting (CF) phenomenon of optimization methods, existing FL approaches are restricted to single task learning and typically assume that data from all nodes are simultaneously available during training. However, in practical IoT scenarios, the data preparation from nodes may be asynchronous, and different tasks require incremental training. To address the issues, we propose a continual FL (CFL) framework with parameter-level importance aggregation (PI-Fed), which supports collaborative task-incremental learning with privacy preservation. Specifically, PI-Fed evaluates the importance of each parameter in the global model to all history tasks, which is computed locally and aggregated at the center server. Then the server performs soft-masking on the averaged gradient collected from local clients based on the parameter importance. By minimizing the change on important parameters, PI-Fed effectively overcomes CF and also achieves high efficiency without experience replay. Extensive experiments on 4 benchmarks with at most 20 sequential tasks demonstrate that our proposed PI-Fed significantly outperforms traditional FL baselines (FedAvg, FedNova, and SCAFFOLD).