Foundation models that bridge vision and language have made significant progress. While they have inspired many life-enriching applications, their potential for abuse in creating new threats remains largely unexplored. In this paper, we reveal that vision-language models (VLMs) can be weaponized to enhance gradient inversion attacks (GIAs) in federated learning (FL), where an FL server attempts to reconstruct private data samples from gradients shared by victim clients. Despite recent advances, existing GIAs struggle to reconstruct high-resolution images when the victim has a large local data batch. One promising direction is to focus reconstruction on valuable samples rather than the entire batch, but current methods lack the flexibility to target specific data of interest. To address this gap, we propose Geminio, the first approach to transform GIAs into semantically meaningful, targeted attacks. It enables a brand new privacy attack experience: attackers can describe, in natural language, the data they consider valuable, and Geminio will prioritize reconstruction to focus on those high-value samples. This is achieved by leveraging a pretrained VLM to guide the optimization of a malicious global model that, when shared with and optimized by a victim, retains only gradients of samples that match the attacker-specified query. Geminio can be launched at any FL round and has no impact on normal training (i.e., the FL server can steal clients' data while still producing a high-utility ML model as in benign scenarios). Extensive experiments demonstrate its effectiveness in pinpointing and reconstructing targeted samples, with high success rates across complex datasets and large batch sizes with resilience against defenses.

With the rapid development of edge computing, devices now offer powerful computing capabilities and diverse applications. However, the surge in smart devices accessing the Internet overwhelms edge servers, which have limited and unevenly distributed resources. This results in challenges like energy management, load balancing (LB), real-time performance, and system complexity. Existing research fails to comprehensively consider these challenges’ combined impact, making it difficult to maximize performance when facing real complex scenarios. To address the above issues, this article proposes an edge cloud resource scheduling scheme based on evolutionary-weighted clustering and transformer-augmented reinforcement learning (EC-TRL). First, server nodes are deployed at the center of user clusters, based on user device locations, to optimize communication delay and evenly distribute resources. Second, the multiobjective scheduling optimization problem under delay constraints is converted into a Markov decision problem, and a deep reinforcement learning method based on soft actor-critic (SAC) is proposed. Finally, actor transformer (AT) and critic transformer (CT) are proposed to improve the network structure of SAC, capture long-term dependencies and complex patterns in long task scheduling sequences, and improve the model’s adaptability and generalization performance in complex dynamic environments. Through comparison experiments with round robin, random, proximal policy optimization, dueling double deep Q-learning network, SAC-L, and SAC-M, the results show that the proposed method improves the optimization performance of energy consumption, LB, and rejection rate of edge cloud resource scheduling by at least 9.57%, 10.90%, and 5.05%.

Resource management challenges frequently manifest in systems and networks as tough online decision tasks, for which the proper solution is dependent on an understanding of the workload and environment and facilitates smooth use of mobile edge and cloud resources. Due to the geographical dispersion of resources, constrained resource capacity, unpredictable nature of tasks, and network hierarchy present in such contexts, it is difficult to efficiently schedule jobs in edge environments. Unfortunately, existing heuristic-based methods lack generality and fast adaptability and thus cannot optimally solve such problems. The advantage actor–critic (A2C) method, on the one hand, can quickly adapt to dynamic circumstances based on relatively few data, and deep reinforcement learning (DRL) agents can on the other hand rapidly learn from their experience of environmental interactions to make better judgments. Therefore, we present an A2C-DRL real-time task scheduling technique for stochastic edge–cloud environments that enables decentralized learning and simultaneous work scheduling across multiple servers. With the aim of producing efficient scheduling decisions, we develop reward values for various resources and model the update policy, server resource scheduling method, and policy learning method. The model is adaptive and includes various hyperparameters that can be adjusted in accordance with the application requirements. We evaluate the load balancing capability of the model by introducing a load balancing factor. Experiments on real datasets show that the proposed A2C-DRL method outperforms seven state-of-the-art algorithms in terms of the reward value, task rejection, and the load balancing factor.

Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic microarchitecture search framework based on graph embeddings. GRL-DSE uses graph representation learning to build a compact and continuous embedding space. Multi-objective Bayesian optimization using an ensemble surrogate model conducts microarchitecture design space exploration in the graph embedding space to efficiently and holistically optimize performance-power-area (PPA) objectives. Experimental studies on RISC-V BOOM show that GRLDSE outperforms previous techniques by 74.59% on Pareto front quality and outperforms manual designs in terms of PPA.

The operational amplifier is a key building block in analog systems. However, the design process of the operational amplifier is time consuming and heavily depends on engineers’ experiences. This article presents OPAMP-Generator, an analog operational amplifier generator, which automates the full design flow from user-defined specifications to GDSII layout without human intervention. OPAMP-Generator includes behavioral-level topology optimization, efficient sizing algorithm based on the classical$ {g_{m}/I_{d}}$design methodology, and automated layout generation. The behavioral-level description of the opamp is represented by the directed acyclic graph (DAG) and a customized variational graph autoencoder is proposed to embed the discrete graph representation into a low-dimensional continuous space. The topology of the opamp can thus be optimized in the latent space, which greatly improves the optimization efficiency. The sizing algorithm based on${g_{m}/I_{d}}$methodology can guarantee the quality of transistor-level circuit implementation. The constraints of the layouts can be naturally derived from the topology level, which facilities the automatic generation of layouts. Experimental results demonstrate that our proposed method can efficiently synthesize operational amplifiers with competitive performances compared to manual designs.

Operational amplifier is a key building block in analog circuits. However, the design process of the operational amplifier is complex and time-consuming, as there are no practical automation tools available in the industry. This paper presents a new topology optimization method for operational amplifiers. The behavioral description of the operational amplifier is described using a directed acyclic graph (DAG), which is then transformed into a low-dimensional embedding in continuous space using a variational graph autoencoder. Topology search is performed in the continuous embedding space using stochastic optimization methods, such as Bayesian Optimization. The yield search results are then transformed back to operational amplifier topologies using a graph decoder. The proposed method is also equipped with a surrogate model for performance prediction. Experimental results show that the proposed approach can achieve significant speedup over the genetic searching algorithms. The produced three-stage operational amplifiers offer competitive performance compared to manual designs.

Operational amplifier is a basic component for analog circuit design. The compensation network of an operational amplifier is crucial to improve the stability of the operational amplifier. In this paper, we present an automated compensation scheme design approach for operational amplifiers. We map the behavioral-level description of the operational amplifier to an acyclic graph and transfer the compensation design problem into a topology optimization problem. A feature mapping method is proposed to encode the graph and a bi-level Bayesian optimization approach is proposed to efficiently solve the topology optimization problem. Experimental results show that our proposed method can obtain competitive three-stage operational amplifiers compared to manual designs.

Predicting drug response based on the genomic profile of a cancer patient is one of the hallmarks of precision oncology. Despite current methods for drug response prediction becoming more accurate, there is still a need to switch from 'black box' predictions to methods that offer high accuracy as well as interpretable predictions. This is of particular importance in real-world applications such as drug response prediction in cancer patients. In this paper, we propose BDKANN, a novel knowledge-based method that employs the hierarchical information on how proteins form complexes and act together in pathways to form the architecture of a deep neural network. We employ BDKANN to predict cancer drug response from cell line gene expression data and our experimental results demonstrate that not only does BDKANN have a low prediction error compared to baseline models but it also allows meaningful interpretation of the network. These interpretations can both explain predictions made and discover novel connections in the biological knowledge that may lead to new hypotheses about mechanisms of drug action.

Bayesian Optimization (BO) is an efficient method for black-box optimization problems. It has been successfully applied to the analog circuit sizing problem. However, all the design variables are viewed as continuous variables in these methods. Actually, many design variables are discrete due to the design rules. In this paper, we proposed an improved BO method for analog circuit sizing with both discrete and continuous variables. We also utilize the Gaussian Process (GP) regression model as the surrogate model for BO. However, we modified the kernel of GP and make it applicable to mixed variables. Experimental results demonstrated that the proposed mixed-variable BO method can significantly reduce the number of simulations with comparable optimization results, compared with the existing BO methods.