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.

Decentralized Federated Graph Learning (DFGL) overcomes the potential bottlenecks of the parameter server in FGL. However, extensive cross-worker communication of graph node embeddings during DFGL training introduces substantial communication costs. To improve communication efficiency, constructing sparse network topologies or applying graph sampling are potential methods. In this paper, we first reveal the bidirectional coupling between network topology construction and graph sampling, underscoring the necessity of their joint optimization. Motivated by this insight, we proposeDuplex, a unified framework that co-optimizes these two components by explicitly modeling their interdependent relationship, thereby significantly reducing communication costs while enhancing training performance in DFGL.Duplexformulates the decision-making process as a coordinated configuration$\langle \mathbf {A}, \mathbf {R} \rangle$, where$\bf {A}$is the adjacency matrix of the network topology and$\bf {R}$denotes the set of graph sampling ratios for workers. However, determining proper coordinated configurations to achieve optimal communication efficiency and training performance (e.g., model accuracy and convergence rate) is challenging due to several practical issues,e.g., statistical heterogeneity and dynamic network conditions. To overcome these challenges,Duplexintroduces a novel learning-driven algorithm to adaptively determine optimal network topologies and graph sampling ratios for workers. Experimental results demonstrate thatDuplexreduces completion time by 20.1%–48.8% and communication costs by 16.7%–37.6% to achieve target accuracy, while improving accuracy by 3.3%–7.9% under identical resource budgets compared to baselines.

Large Language Model (LLM)-based Multi-agent Systems (MAS) have demonstrated remarkable capabilities in various complex tasks, ranging from collaborative problem-solving to autonomous decision-making. However, as these systems become increasingly integrated into critical applications, their vulnerability to adversarial attacks, misinformation propagation, and unintended behaviors have raised significant concerns. To address this challenge, we introduce G-Safeguard, a topology-guided security lens and treatment for robust LLM-MAS, which leverages graph neural networks to detect anomalies on the multi-agent utterance graph and employ topological intervention for attack remediation. Extensive experiments demonstrate that G-Safeguard: (I) exhibits significant effectiveness under various attack strategies, recovering over 40% of the performance for prompt injection; (II) is highly adaptable to diverse LLM backbones and large-scale MAS; (III) can seamlessly combine with mainstream MAS with security guarantees.

Humans can complete high-quality creative work, such as drawing a picture or creating a video based on text, and editing images or videos according to textual requirements. In the earlier period of artificial intelligence, the “best of N” strategy was often utilized to leverage the creative capability of multiple visual creators, which was computationally inefficient and labor-intensive. With the emergence of large language models (LLMs), the LLM-based agent dynamically plans the invocation of tools to accomplish creative tasks. However, these agent systems struggle to achieve optimal tool planning and creative performance, especially complex creative tasks. Toward these issues, we propose MAGiC, a LLM-Powered Multi-Agent Framework for Visual Generation and Editing to unleash Visual Creativity. MAGiC addresses users’ creation requirements through the collaboration of four modules, i.e., task assignment, planning, execution, and evaluation, with each controlled by agents configured for different roles. Specifically, the task assignment module iteratively releases new tasks based on the user requirements and its completion progress. The Planning module configures the corresponding Planner for different types of tasks, and these Planners create detailed plans for the tasks they are responsible for. The Execution module iteratively executes the plans set by the Planners. The evaluation module assesses the result obtained by the execution module to prevent errors from affecting subsequent tasks. Finally, MAGiC illustrates excellent creativity on multiple tasks, especially complex ones, and the high scalability of MAGiC is an initial step in applying multi-agent systems to llm-based visual system.

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.

Federated Learning (FL) enables distributed clients to collaboratively train models without exposing their private data. However, it is difficult to implement efficient FL due to limited resources. Most existing works compress the transmitted gradients or prune the global model to reduce the resource cost, but leave the compressed or pruned parameters under-optimized, which degrades the training performance. To address this issue, the neural composition technique constructs size-adjustable models by composing low-rank tensors, allowing every parameter in the global model to learn the knowledge from all clients. Nevertheless, some tensors can only be optimized by a small fraction of clients, thus the global model may get insufficient training, leading to a long completion time, especially in heterogeneous edge scenarios. To this end, we enhance the neural composition technique, enabling all parameters to be fully trained. Further, we propose a lightweight FL framework, called Heroes, with enhanced neural composition and adaptive local update. A greedy-based algorithm is designed to adaptively assign the proper tensors and local update frequencies for participating clients according to their heterogeneous capabilities and resource budgets. On this basis, we further propose an extension of Heroes, termed AdaHeroes, which further improves the training performance under the statistical heterogeneity scenario based on an adaptive client selection strategy. Extensive experiments demonstrate that Heroes can reduce traffic consumption by about 72.46% and provide up to$2.76\times $speedup compared to the baselines. Furthermore, with the setting of statistical heterogeneity, AdaHeroes can improve the test accuracy by about 4.77% compared with Heroes and the baselines.

Efficiently modeling spatio-temporal (ST) physical processes and observations presents a challenging problem for the deep learning community. Many recent studies have concentrated on meticulously reconciling various advantages, leading to designed models that are neither simple nor practical. To address this issue, this paper presents a systematic study on existing shortcomings faced by off-the-shelf models, including lack of local fidelity, poor prediction performance over long time-steps, low scalability, and inefficiency. To systematically address the aforementioned problems, we propose an EarthFarseer, a concise framework that combines parallel local convolutions and global Fourier-based transformer architectures, enabling dynamically capture the local-global spatial interactions and dependencies. EarthFarseer also incorporates a multi-scale fully convolutional and Fourier architectures to efficiently and effectively capture the temporal evolution. Our proposal demonstrates strong adaptability across various tasks and datasets, with fast convergence and better local fidelity in long time-steps predictions. Extensive experiments and visualizations over eight human society physical and natural physical datasets demonstrates the state-of-the-art performance of EarthFarseer. We release our code at https://github.com/easylearningscores/EarthFarseer.

Federated Learning (FL) enables distributed clients to collaboratively train models without exposing their private data. However, it is difficult to implement efficient FL due to limited resources. Most existing works compress the transmitted gradients or prune the global model to reduce the resource cost, but leave the compressed or pruned parameters under-optimized, which degrades the training performance. To address this issue, the neural composition technique constructs size-adjustable models by composing low-rank tensors, allowing every parameter in the global model to learn the knowledge from all clients. Nevertheless, some tensors can only be optimized by a small fraction of clients, thus the global model may get insufficient training, leading to a long completion time, especially in heterogeneous edge scenarios. To this end, we enhance the neural composition technique, enabling all parameters to be fully trained. Further, we propose a lightweight FL framework, called Heroes, with enhanced neural composition and adaptive local update. A greedy-based algorithm is designed to adaptively assign the proper tensors and local update frequencies for participating clients according to their heterogeneous capabilities and resource budgets. Extensive experiments demonstrate that Heroes can reduce traffic consumption by about 72.05% and provide up to 2.97× speedup compared to the baselines.

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.

Spatio-temporal (ST) prediction has garnered a De facto attention in earth sciences, such as meteorological prediction, human mobility perception. However, the scarcity of data coupled with the high expenses involved in sensor deployment results in notable data imbalances. Furthermore, models that are excessively customized and devoid of causal connections further undermine the generalizability and interpretability. To this end, we establish a causal framework for ST predictions, termed CaPaint, which targets to identify causal regions in data and endow model with causal reasoning ability in a two-stage process. Going beyond this process, we utilize the back-door adjustment to specifically address the sub-regions identified as non-causal in the upstream phase. Specifically, we employ a novel image inpainting technique. By using a fine-tuned unconditional Diffusion Probabilistic Model (DDPM) as the generative prior, we in-fill the masks defined as environmental parts, offering the possibility of reliable extrapolation for potential data distributions. CaPaint overcomes the high complexity dilemma of optimal ST causal discovery models by reducing the data generation complexity from exponential to quasi-linear levels. Extensive experiments conducted on five real-world ST benchmarks demonstrate that integrating the CaPaint concept allows models to achieve improvements ranging from 4.3% to 77.3%. Moreover, compared to traditional mainstream ST augmenters, CaPaint underscores the potential of diffusion models in ST enhancement, offering a novel paradigm for this field. Our project is available at https://anonymous.4open.science/r/12345-DFCC.

To approach the challenges of non-IID data and limited communication resource raised by the emerging federated learning (FL) in mobile edge computing (MEC), we propose an efficient framework, calledFedMigr, which integrates a deep reinforcement learning (DRL) based model migration strategy into the pioneer FL algorithmFedAvg. According to the data distribution and resource budgets, ourFedMigrwill intelligently guide one client to forward its local model to another client after local updating, before directly sending the local models to the server for global aggregation as inFedAvg. Intuitively, migrating a local model from one client to another is equivalent to training the model over more data from different clients, alleviating the influence of non-IID issue. To this end, we propose an experience-driven method to make proper decisions for model migrations while satisfying the resource constraints. We also prove thatFedMigrcan help to reduce the parameter divergences between different local models and the global model from a theoretical perspective under the non-IID setting. Extensive experiments on three popular benchmark datasets demonstrate thatFedMigrcan achieve an average accuracy improvement of around 13%, and reduce bandwidth consumption for global communication by 42% on average, compared with the baselines.