Although large-scale pre-trained models hold great potential for adapting to
downstream tasks through fine-tuning, the performance of such fine-tuned models
is often limited by the difficulty of collecting sufficient high-quality,
task-specific data. Federated Learning (FL) offers a promising solution by
enabling fine-tuning across large-scale clients with a variety of task data,
but it is bottlenecked by significant communication overhead due to the
pre-trained models' extensive size. This paper addresses the high communication
cost for fine-tuning large pre-trained models within FL frameworks through
low-rank fine-tuning. Specifically, we train a low-rank adapter for each
individual task on the client side, followed by server-side clustering for
similar group of adapters to achieve task-specific aggregation. Extensive
experiments on various language and vision tasks, such as GLUE and
CIFAR-10/100, reveal the evolution of task-specific adapters throughout the FL
training process and verify the effectiveness of the proposed low-rank
task-specific adapter clustering (TAC) method.

通过低秩适配器进行通信开销较小的模型微调以解决大规模预训练模型在联邦学习框架中的高通信成本问题。

FL-TAC: 通过低秩、任务特定的适配器聚类增强联邦学习中的微调

FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank,  Task-Specific Adapter Clustering

Large Language Models (LLMs) have revolutionized open-domain dialogue agents
but encounter challenges in multi-character role-playing (MCRP) scenarios. To
address the issue, we present Neeko, an innovative framework designed for
efficient multiple characters imitation. Unlike existing methods, Neeko employs
a dynamic low-rank adapter (LoRA) strategy, enabling it to adapt seamlessly to
diverse characters. Our framework breaks down the role-playing process into
agent pre-training, multiple characters playing, and character incremental
learning, effectively handling both seen and unseen roles. This dynamic
approach, coupled with distinct LoRA blocks for each character, enhances
Neeko's adaptability to unique attributes, personalities, and speaking
patterns. As a result, Neeko demonstrates superior performance in MCRP over
most existing methods, offering more engaging and versatile user interaction
experiences. Code and data are available at
this https URL

Neeko 是一个创新的框架，采用动态低秩适配器策略，能够适应不同的角色，在多角色扮演中表现出更高的性能，提供更加丰富和多样化的用户交互体验。