Compression is an efficient way to relieve the tremendous communication overhead of federated learning (FL) systems. However, for the existing works, the information loss under compression will lead to unexpected model/gradient deviation for the FL training, significantly degrading the training performance, especially under the challenges of data heterogeneity and model obsolescence. To strike a delicate trade-off between model accuracy and traffic cost, we propose Caesar, a novel FL framework with a low-deviation compression approach. For the global model download, we design a greedy method to optimize the compression ratio for each device based on the staleness of the local model, ensuring a precise initial model for local training. Regarding the local gradient upload, we utilize the device's local data properties (i.e., sample volume and label distribution) to quantify its local gradient's importance, which then guides the determination of the gradient compression ratio. We have implemented Caesar, on two physical platforms with 40 smartphones and 80 NVIDIA Jetson devices. Extensive results show that Caesar, can reduce the traffic costs by about 25.54%þicksim37.88% when achieving the same target accuracy compared to the compression-based baselines, while incurring only a 0.68% degradation in final test accuracy relative to the full-precision communication.

With the rapid evolution of Large Language Models (LLMs), LLMbased agents and Multi-agent Systems (MAS) have significantly expanded the capabilities of LLM ecosystems.This evolution stems from empowering LLMs with additional modules such as memory, tools, environment, and even other agents.However, this advancement has also introduced more complex issues of trustworthiness, which previous research focusing solely on LLMs could not cover.In this survey, we propose the TrustAgent framework, a comprehensive study on the trustworthiness of agents, characterized by modular taxonomy, multi-dimensional connotations, and * Miao Yu and Fanci Meng contribute equally to this paper.

Despite Graph Neural Networks (GNNs) demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with overfitting and over-smoothing as they go deeper as models of computer vision (CV) realm.The success of artificial intelligence in computer vision and natural language processing largely stems from its ability to train deep models effectively.We have thus conducted a systematic study on deep GNN models.Our findings indicate that the current success of deep GNNs primarily stems from (I) the adoption of innovations from CNNs, such as residual/skip connections, or (II) the tailor-made aggregation algorithms like DropEdge.However, these algorithms often lack intrinsic interpretability and indiscriminately treat all nodes within a given layer in a similar manner, thereby failing to capture the nuanced differences among various nodes.In this paper, we introduce the Snowflake Hypothesis -a novel paradigm underpinning the concept of "one node, one receptive field".The hypothesis draws inspiration from the unique and individualistic patterns of * Contribute equally to this research.