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arXiv:2609.06346v1 Announce Type: new Abstract: Continual learning (CL) enables models to acquire new knowledge from sequentially arriving tasks while retaining previously learned knowledge. However,...
arXiv:2601. 07177v5 Announce Type: replace-cross Abstract: Federated learning (FL) addresses privacy and data-silo issues in the training of large language models (LLMs).
FedGraph is a graph-based aggregation framework designed to defend federated large language model fine-tuning against backdoor attacks. It exploits the observation that malicious clients produce consistently similar updates, while benign clients generate diverse ones, by modeling client updates as nodes in a dynamically constructed graph and extracting structural features. The resulting compact client representations enable effective identification and suppression of malicious contributions, achieving high detection accuracy and low false positives in extensive experiments.
FedReview is a review-based mechanism for federated learning that identifies and removes poisoned model updates without needing a server-side validation dataset or historic client knowledge. In each training round, the server randomly selects reviewers who evaluate incoming updates on their own training data, rank them, and estimate how many low-quality updates are likely poisoned. The server then aggregates these rankings via majority voting to filter out suspicious updates during model aggregation, enabling robust global model training even in adversarial settings.
arXiv:2605. 05704v3 Announce Type: replace-cross Abstract: Recent advances in foundation models have transformed LLMs from passive conversational systems into autonomous agents capable of reasoning and tool execution.
arXiv:2605. 07961v2 Announce Type: replace Abstract: Federated fine-tuning (FFT) has emerged as a privacy-preserving paradigm for collaboratively adapting large language models (LLMs).