7 papers
On Safety Risks in Experience-Driven Self-Evolving Agents
Weixiang Zhao, Yichen Zhang, Yingshuo Wang +8
Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduc…
SABlock: Semantic-Aware KV Cache Eviction with Adaptive Compression Block Size
Jinhan Chen, Jianchun Liu, Hongli Xu +2
The growing memory footprint of the Key-Value (KV) cache poses a severe scalability bottleneck for long-context Large Language Model (LLM) inference. While KV cache eviction has em…
Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data
Shilong Wang, Jianchun Liu, Hongli Xu +3
Decentralized Federated Graph Learning (DFGL) overcomes potential bottlenecks of the parameter server in FGL by establishing a peer-to-peer (P2P) communication network among worker…
Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
Yujia Huo, Jianchun Liu, Hongli Xu +3
Federated fine-tuning (FedFT) of large language models (LLMs) has emerged as a promising solution for adapting models to distributed data environments while ensuring data privacy.…
Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout
Shilong Wang, Jianchun Liu, Hongli Xu +2
Fine-tuning plays a crucial role in enabling pre-trained LLMs to evolve from general language comprehension to task-specific expertise. To preserve user data privacy, federated fin…
A Robust Federated Learning Framework for Undependable Devices at Scale
Shilong Wang, Jianchun Liu, Hongli Xu +4
In a federated learning (FL) system, many devices, such as smartphones, are often undependable (e.g., frequently disconnected from WiFi) during training. Existing FL frameworks alw…