Recent self-supervised stereo matching methods have made significant progress. They typically rely on the photometric consistency assumption, which presumes corresponding points across views share the same appearance. However, this assumption could be compromised by real-world disturbances, resulting in invalid supervisory signals and a significant accuracy gap compared to supervised methods. To address this issue, we propose SMFormer, a framework integrating more reliable self-supervision guided by the Vision Foundation Model (VFM) and data augmentation. We first incorporate the VFM with the Feature Pyramid Network (FPN), providing a discriminative and robust feature representation against disturbance in various scenarios. We then devise an effective data augmentation mechanism that ensures robustness to various transformations. The data augmentation mechanism explicitly enforces consistency between learned features and those influenced by illumination variations. Additionally, it regularizes the output consistency between disparity predictions of strong augmented samples and those generated from standard samples. Experiments on multiple mainstream benchmarks demonstrate that our SMFormer achieves state-of-the-art (SOTA) performance among self-supervised methods and even competes on par with supervised ones. Remarkably, in the challenging Booster benchmark, SMFormer even outperforms some SOTA supervised methods, such as CFNet.

How to defend against attacks in Federated Partial Label Learning (FedPLL) is a brand new and challenging question in machine learning security due to stealthy and efficient attack behaviors of adversaries. In this paper, we systematically study this problem by developing an Adaptive Partial Label Attack (APLA) which subtly manipulates the candidate label set of the data sample. To defend against APLA, we develop the RobustFedPLL framework incorporating three modules: (1) in preliminary clustering, we implement a Gaussian Mixture Model (GMM) and a moving average mechanism to identify clients' confidence; (2) in representation contrasting, we develop a contrast-based algorithm to obtain clients' model feature representations; (3) in final clustering, we utilize mainstream clustering algorithms to finally distinguish adversaries. Experiments of RobustFedPLL and SOTA defense algorithms based on two datasets are conducted, demonstrating the superiority of RobustFedPLL under various experimental settings.

In this paper, we propose pFedMo, a personalized federated learning algorithm with contrastive momentum. In pFedMo, we design a score function to personalize worker models by distilling knowledge from the aggregator's representation model so as to address the non-i.i.d. issue. To accelerate the convergence, we leverage the momentum acceleration on both the worker side and the aggregator side. However, the typical momentum without personalization does not suit well for the worker models with personalization, influencing convergence performance. To address this, we develop a personalized/contrastive momentum method for efficient momentum acceleration. We provide mathematical proof for the convergence of pFedMo on non-i.i.d. data. Extensive experiments based on real-world datasets and IoT system are conducted, verifying that pFedMo outperforms existing mainstream benchmarks, and achieves up to 35.90% accuracy increase and 3.64x training time speedup under a wide range of settings.

Algorithmic fairness, emphasizing that machine learning algorithms should not discriminate against specific demographic groups, has recently gained increasing attention in the context of federated learning. Existing algorithmic fairness algorithms in federated learning primarily focus on mitigating inherent biases within the training data or biases introduced by non-IID data distributions. However, these methods heavily rely on the assumption of trust and cooperation among participants in federated learning, leaving a notable gap in exploring the robustness of algorithmic fairness under adversarial environments. Therefore, our study advocates the exploration of the robustness of algorithmic fairness in federated learning. We first introduce four types of fairness attacks targeting the fairness of federated learning models. Then, we propose FairGuard, an innovative defense framework designed to counter these attacks. FairGuard employs a novel dual-inference algorithm that effectively identifies malicious clients during the global aggregation phase of federated learning. Our comprehensive experiments demonstrate that the introduced fairness attacks significantly compromise the global model's fairness and cannot be effectively mitigated by existing defense mechanisms, while FairGuard exhibits remarkable effectiveness in defending against these fairness attacks.

Predicting stock trends is a highly rewarding but high-risk endeavor due to the complex interplay of market dynamics, irrational behaviors, and diverse sentiments. Previous studies have used time-series analysis on historical prices or sentiment analysis on textual information. However, these methods often fail to capture the dynamic interactions between text and time-series modalities and overlook the different perspectives embedded in textual data. To address these limitations, we propose the pleno-alignment framework (PAFrame) that enhances multimodal stock information through intermodal and intramodal alignment to capture market dynamics. Our framework first integrates textual and time-series data in a shared representation space to learn modal-invariant information. To tackle divergent sentiments in textual data, we employ a contrastive learning approach to extract abstract semantic meanings from objective and subjective perspectives, thereby improving the robustness of language representations. Finally, we use a hybrid approach that explicitly combines cross-attention mechanisms to create a unified representation and utilizes prompts to implicitly guide language models with numerical financial indicators for final prediction. Our comprehensive experiments on five real-world datasets show that PAFrame outperforms existing methods in predicting stock trends.

In this paper, we propose and analyze HierAdMo, a three-tier adaptive momentum accelerated client-edge-cloud Federated Learning (FL) algorithm. HierAdMo combines the momentum acceleration on both worker and edge levels. However, simply combining these two levels of momenta may lead to disagreement between them, negatively influencing convergence performance. To this end, we embed an online adaptive method that scales down the momentum when disagreement occurs. We provide mathematical proof for the convergence of HierAdMo for non-i.i.d. data and the tighter convergence upper bound compared with a version of HierAdMo without adaptation (HierAdMo-R). Finally, extensive experiments based on real-world datasets are conducted, verifying that HierAdMo outperforms existing mainstream benchmarks and achieves the optimal or near-optimal convergence performance compared with HierAdMo-R under a wide range of settings.

In this article, we propose Hierarchical Federated Learning with Momentum Acceleration (HierMo), a three-tier worker-edge-cloud federated learning algorithm that applies momentum for training acceleration. Momentum is calculated and aggregated in the three tiers. We provide convergence analysis for HierMo, showing a convergence rate of$\mathcal {O}(\frac{1}{T})$. In the analysis, we develop a new approach to characterize model aggregation, momentum aggregation, and their interactions. Based on this result, we prove that HierMo achieves a tighter convergence upper bound compared with HierFAVG without momentum. We also propose HierOPT, which optimizes the aggregation periods (worker-edge and edge-cloud aggregation periods) to minimize the loss given a limited training time. By conducting the experiment, we verify that HierMo outperforms existing mainstream benchmarks under a wide range of settings. In addition, HierOPT can achieve a near-optimal performance when we test HierMo under different aggregation periods.

Federated learning (FL) is a fast-developing technique that allows multiple workers to train a global model based on a distributed dataset. Conventional FL (FedAvg) employs gradient descent algorithm, which may not be efficient enough. Momentum is able to improve the situation by adding an additional momentum step to accelerate the convergence and has demonstrated its benefits in both centralized and FL environments. It is well-known that Nesterov Accelerated Gradient (NAG) is a more advantageous form of momentum, but it is not clear how to quantify the benefits of NAG in FL so far. This motives us to propose FedNAG, which employs NAG in each worker as well as NAG momentum and model aggregation in the aggregator. We provide a detailed convergence analysis of FedNAG and compare it with FedAvg. Extensive experiments based on real-world datasets and trace-driven simulation are conducted, demonstrating that FedNAG increases the learning accuracy by 3–24% and decreases the total training time by 11–70% compared with the benchmarks under a wide range of settings.

Fog computing is a promising solution to provide low-latency and ubiquitously available computation offloading services to widely distributed Internet of Things (IoT) devices with limited computing capabilities. One obstacle, however, is how to seamlessly hand over mobile IoT devices among different fog nodes to avoid service interruption. In this paper, we propose seamless fog (sFog), a new framework supporting efficient congestion control and seamless handover schemes. Intrinsically, sFog improves system performance during handovers (achieved by the handover scheme), and guarantees the performance does not degrade when handovers do not occur (achieved by the congestion control scheme). Through the congestion control scheme, jobs are efficiently offloaded without causing unnecessary system idling; through the handover scheme, jobs are pre-migrated to the target fog node when a handover is about to occur, in order to reduce migration delay. In order to evaluate the performance of sFog, we propose a theoretical framework and establish a real-world prototype. Both the theoretical and experimental results show that sFog achieves substantial delay reductions compared with traditional benchmark handover schemes.