Federated deep learning (FDL) is a promising privacy-preserving approach for training deep neural networks on distributed datasets without raw data sharing. But the classical synchronous FDL faces straggler problem: slow trainers severely impede overall efficiency. Inspired by speculative execution techniques in modern processors, this paper proposes SwiftFL, a novel and efficient speculative training system for FDL. Instead of simply waiting for slower trainer, SwiftFL proactively updates the global model with predicted gradients, enabling faster trainers to speculatively initiate the next training round. Furthermore, a gradient compensation technique is proposed to correct mispredicted training without re-training. Finally, to overcome the model-drift problem caused by fast trainers perform more local training rounds, we propose a client selection strategy. This strategy determines whether trainers should perform speculative training by striking a balance between two metrics: model drift degree and local training efficiency. In the evaluation, we compare SwiftFL with four state-of-the-art FDL systems and demonstrate that SwiftFL achieves an average speedup of 6.08× while maintaining consistent final model accuracy.

Abstract Ransomware has become one of the most destructive cyberattacks worldwide in recent years and has caused billions of dollars. Numerous defense mechanisms have been proposed to mitigate the ransomware threat. However, as attack technologies advance, ransomware has rapidly evolved. It employs sophisticated techniques to evade traditional defense mechanisms and has lead to devastating impacts on organizations globally. While many studies have focused on ransomware and its defense, none have provided a comprehensive overview of the ongoing battle between defense mechanisms and the evolving ransomware. They also do not explore the techniques employed by ransomware authors to evade detection. To fill this gap and motivate further research, we conduct an extensive investigation into evasive ransomware, including the common techniques they employ and the efforts researchers have made to counter them. Based on this, we offer a preliminary exploration of potential defense concepts that track multi-level events across different attack stages and leverage the correlations between them to construct the attack flow. This paper helps researchers in the ransomware field gain a comprehensive understanding of evasive ransomware. It also offers insights for future researchers to enhance defense mechanisms to cover the potential threats identified in this study, minimizing the losses caused by evasive ransomware.

With the growing use of large language models (LLMs) hosted on cloud platforms to offer inference services, privacy concerns about the potential leakage of sensitive information are escalating. Secure Multi-Party Computation (MPC) is a promising solution to protect the privacy in LLM inference. However, MPC requires frequent inter-server communication, causing high performance overhead. Inspired by the prevalent activation sparsity of LLMs, where most neuron are not activated after non-linear activation functions, we propose an efficient private inference system, Comet. This system employs an accurate and fast predictor to predict the sparsity distribution of activation function output. Additionally, we introduce a new private inference protocol. It efficiently and securely avoids computations involving zero values by exploiting the spatial locality of the predicted sparsity distribution. While this computation-avoidance approach impacts the spatiotemporal continuity of KV cache entries, we address this challenge with a low-communication overhead cache refilling strategy that merges miss requests and incorporates a prefetching mechanism. Finally, we evaluate Comet on four common LLMs and compare it with six state-of-the-art private inference systems. Comet achieves a$1.87\times-2.63\times$speedup and a$1.94\times-2.64\times$communication reduction.

Federated tree-based models are popular in many real-world applications owing to their high accuracy and good interpretability. However, the classical synchronous method causes inefficient federated tree-based model training due to tree node dependencies. Inspired by speculative execution techniques in modern high-performance processors, this paper proposes FTSeir, a novel and efficient speculative federated learning system. Instead of simply waiting, FTSeir optimistically predicts the outcome of the prior tree node. By resolving tree node dependencies with a neural network-based split point predictor, the training tasks of child tree nodes can be executed speculatively in advance via separate threads. This speculation enables cross-layer concurrent training, thus significantly reducing the waiting time. Furthermore, we propose an eager verification mechanism to promptly identify mispredictions, thereby reducing wasted computing resources. On a misprediction, an incomplete rollback is triggered for quick recovery by reusing the output of the mis-speculative training, which reduces computational requirements. We implement FTSeir and evaluate its efficiency in a real-world federated learning setting with six public datasets. Evaluation results demonstrate that FTSeir achieves up to 3.45× and 3.60× speedup over the state-of-the-art gradient boosted decision trees and random forests implementations, respectively.

Federated tree-based models are popular in many real-world applications owing to their high accuracy and good interpretability. However, the classical synchronous method causes inefficient federated tree model training due to tree node dependencies. Inspired by speculative execution techniques in modern high-performance processors, this paper proposes SpecFL, a novel and efficient speculative federated learning system. Instead of simply waiting, SpecFL optimistically predicts the outcome of the prior tree node. By resolving tree node dependencies with a split point predictor, the training tasks of child tree nodes can be executed speculatively in advance via separate threads. This speculation enables cross-layer concurrent training, thus significantly reducing the waiting time. Furthermore, we propose a greedy speculation policy to exploit speculative training for deeper inter-layer concurrent training and an eager rollback mechanism for lossless model quality. We implement SpecFL and evaluate its efficiency in a real-world federated learning setting with six public datasets. The evaluation results demonstrate that SpecFL can be 2.08-3.33x and 2.14-3.44x faster than the state-of-the-art GBDT and RF implementations, respectively.

Federated Learning (FL) is a promising approach to privacy-preserving machine learning. However, recent works reveal that gradients can leak private data. Using trusted SGX-processors for this task yields gradient-preserving but requires to prevent exploitation of any side-channel attacks.

With its huge real-world demands, large-scale confidential computing still cannot be supported by today's Trusted Execution Environment (TEE), due to the lack of scalable and effective protection of high-throughput accelerators like GPUs, FPGAs, and TPUs etc. Although attempts have been made recently to extend the CPU-like enclave to GPUs, these solutions require change to the CPU or GPU chips, may introduce new security risks due to the side-channel leaks in CPU-GPU communication and are still under the resource constraint of today's CPU TEE.To address these problems, we present the first Heterogeneous TEE design that can truly support large-scale compute or data intensive (CDI) computing, without any chip-level change. Our approach, called HETEE, is a device for centralized management of all computing units (e.g., GPUs and other accelerators) of a server rack. It is uniquely designed to work with today's data centres and clouds, leveraging modern resource pooling technologies to dynamically compartmentalize computing tasks, and enforce strong isolation and reduce TCB through hardware support. More specifically, HETEE utilizes the PCIe ExpressFabric to allocate its accelerators to the server node on the same rack for a non-sensitive CDI task, and move them back into a secure enclave in response to the demand for confidential computing. Our design runs a thin TCB stack for security management on a security controller (SC), while leaving a large set of software (e.g., AI runtime, GPU driver, etc.) to the integrated microservers that operate enclaves. An enclaves is physically isolated from others through hardware and verified by the SC at its inception. Its microserver and computing units are restored to a secure state upon termination.We implemented HETEE on a real hardware system, and evaluated it with popular neural network inference and training tasks. Our evaluations show that HETEE can easily support the CDI tasks on the real-world scale and incurred a maximal throughput overhead of 2.17% for inference and 0.95% for training on ResNet152.