Long-context question-answering tasks require models to locate a small amount of scattered evidence within lengthy, sparse, and noisy inputs, and to integrate information across multiple fragments during reading. Existing recurrent reading and memory-updating methods provide an effective paradigm for handling ultra-lon...
Jia-Hui Ling, Wei-Dong Bao, Zheng-Yi Zhong et al.· 2026 12th International Conf...· 0 citations
Federated Continual Learning (FCL) enables distributed clients to collaboratively learn a sequence of tasks while preserving data privacy and mitigating catastrophic forgetting. However, most existing FCL methods rely on the assumption that all clients share an identical model architecture, which is impractical in real...
Pei-Yi Zeng, Shu-Ming Yang, Jia-Wei Liao et al.· 2026 12th International Conf...· 0 citations
Client-level federated unlearning seeks to update an already trained global model so that the influence of a specified client is weakened, while the model remains effective for the remaining clients. Existing methods are mostly designed for homogeneous model settings and often rely on retraining, historical updates, or...
Jing-Yi Leng, Zheng-Yi Zhong, Hai-Lu Xin et al.· 2026 12th International Conf...· 0 citations
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