Playmate2: Training-Free Multi-Character Audio-Driven Animation via Diffusion Transformer with Reward Feedback

FAPE-IR introduces a Frequency-Aware Planning and Execution framework for All-in-One Image Restoration (AIO-IR), designed to address multiple image degradations in complex conditions. Unlike existing methods that depend on task-specific designs, FAPE-IR utilizes a frozen Multimodal Large Language Model (MLLM) to analyze degraded images and create frequency-aware restoration plans. These plans guide a LoRA-based Mixture-of-Experts (LoRA-MoE) module, which dynamically selects experts based on the frequency features of the input image, enhancing restoration quality through adversarial training an…

The article discusses the evaluation of backdoor attacks against federated model adaptation, particularly focusing on the impact of Parameter-Efficient Fine-Tuning techniques like Low-Rank Adaptation (LoRA). It highlights the security threats posed by backdoor attacks during local training phases and presents findings on backdoor lifespan, indicating that lower LoRA ranks can lead to longer persistence of backdoors. This research emphasizes the need for improved evaluation methods to address these vulnerabilities in Federated Learning.

The article presents FedALT, a new algorithm for federated fine-tuning of large language models (LLMs) that addresses the challenges of cross-client interference and data heterogeneity. Traditional methods, primarily based on FedAvg, often lead to suboptimal personalization due to model aggregation issues. FedALT allows each client to continue training its individual LoRA while integrating knowledge from a separate Rest-of-World (RoW) LoRA component. This approach includes an adaptive mixer to balance local adaptation with global information effectively.