ABSTRACT Federated learning (FL) in medical image analysis offers privacy‐preserving collaborative training but faces challenges such as the impact of Non‐IID data on model performance and communication overhead that limits system efficiency. Existing methods often struggle to balance data heterogeneity with privacy protection in high‐dimensional pathological image classification tasks. To overcome these challenges, we propose a Privacy‐Preserving Self‐Distillation Federated Framework (PPFedKD) that enhances both model performance and privacy security through optimized feature learning and communication aggregation. The proposed framework introduces a adaptive contrastive distillation strategy, leveraging spatial and semantic augmentations to produce consistent feature representations and mitigate imbalances in client data distributions. It also employs a semi‐asynchronous dynamic aggregation mechanism with a lag threshold, allowing the server to accept slightly outdated updates, thereby reducing communication redundancy and wait times. Furthermore, privacy‐enhanced gradient perturbation is applied on the client side using a noise constraint strategy to effectively mitigate privacy leakage while ensuring stable convergence. Experimental results on diabetic retinopathy and breast cancer pathology datasets demonstrate that PPFedKD outperforms baseline methods in classification accuracy, privacy protection, and communication efficiency, providing a secure and effective solution for medical image classification.

Robot-assisted minimally invasive surgery is widely used because of its superior postoperative recovery outcomes. However, the workload for surgeons remains high. The development of autonomous suturing capabilities in surgical robots is poised to significantly reduce surgeon workload. In this study, we present a novel method or autonomous suturing using a minimally invasive surgical robot. We quantify the surgical suturing requirements and propose corresponding metrics for evaluating the suturing effect. We also use the dynamic adjustment of stitch position to optimize the surgical robot autonomous suturing scheme. Furthermore, we employ particle swarm algorithms to enhance the grasping posture of surgical instruments, enabling the robot to achieve optimal suture needle clamping. Our method maintains the same level of expert operator in the suturing parametric index of suturing when suturing two types of wounds: gauze and egg membrane. The autonomous suturing method proposed in this study is currently deployed on our own surgical robot, and it can be generalized to other surgical robots. This will lay the foundation for surgical robots to achieve fully autonomous surgery. The experimental results show that the stitching effect of our proposed autonomous robot stitching method is already close to that of surgeons using the same robot, and it maintains good consistency in multiple sets of experiments. The method proposed in this study can be generalized to various other surgical robots, laying the foundation for surgical robots to achieve fully autonomous surgery.

Flexible surgical instruments can flexibly adjust their posture with a high degree of freedom, which makes them highly suitable for performing surgical tasks in narrow workspaces. However, redundant degrees of freedom increase their kinematic difficulty, which may cause redundant solutions, complex calculations, and low speeds. In this paper, a flexible surgical instrument is presented. The structural characteristics of this flexible instrument were explored in terms of force balance, it was concluded that the instrument had a constant curvature during bending. Based on this, the kinematics and inverse kinematics were solved via the geometric and Newton iteration methods, respectively. Our experiments showed that the proposed method for solving flexible instrument kinematics had high precision, a unique solution, and high speed; the instrument can be well controlled to perform refined operations. The proposed geometric method for solving the flexible instrument kinematics avoided the calculation of the Jacobian matrix, making it fast and capable of meeting the master-slave control requirement for real-time surgery. Furthermore, the proposed kinematics solution method is not limited by the mechanical structure, so it can be used for flexible instruments owning to its constant curvature bending.

The detection of performance changes in software change roll-outs in Internet-based services is crucial for an operations team, because it allows timely roll-back of a software change when performance degrades unexpectedly. However, it is infeasible to manually investigate millions of performance measurements of many roll-outs. In this paper, we present an automated tool, FUNNEL, for rapid and robust impact assessment of software changes in large Internet-based services. FUNNEL automatically collects the related performance measurements for each software change. To detect significant performance behavior changes, FUNNEL adopts singular spectrum transform (SST) algorithm as the core algorithm, uses various techniques to improve its robustness and reduce its computational cost, and applies a difference-in-difference (DiD) method to differentiate the true causality from the random correlations between the performance change and the software change. Evaluation through historical data in real-word services shows that FUNNEL achieves accuracy of more than 99.7 percent. Compared with previous methods, FUNNEL's detection delay is 38.02 to 64.99 percent shorter, and its computation speed is 4.59-7,098 times faster. In real deployment, FUNNEL achieves a 98.21 percent precision, high robustness, fast detection speed, and shows its capability in detecting unexpected behavior changes.

Closely monitoring service performance and detecting anomalies are critical for Internet-based services. However, even though dozens of anomaly detectors have been proposed over the years, deploying them to a given service remains a great challenge, requiring manually and iteratively tuning detector parameters and thresholds. This paper tackles this challenge through a novel approach based on supervised machine learning. With our proposed system, Opprentice (Operators' apprentice), operators' only manual work is to periodically label the anomalies in the performance data with a convenient tool. Multiple existing detectors are applied to the performance data in parallel to extract anomaly features. Then the features and the labels are used to train a random forest classifier to automatically select the appropriate detector-parameter combinations and the thresholds. For three different service KPIs in a top global search engine, Opprentice can automatically satisfy or approximate a reasonable accuracy preference (recall >= 0.66 and precision>= 0.66). More importantly, Opprentice allows operators to label data in only tens of minutes, while operators traditionally have to spend more than ten days selecting and tuning detectors, which may still turn out not to work in the end.

When using search engines, users often care about search response time (SRT) in addition to result accuracy. It is thus the operators' responsibility to closely monitor and improving SRT. The first critical step of improving SRT is to pinpoint the root causes of slow SRT. However, this task is very challenging because SRT can be impacted by many factors, e.g., networks, data centers, browsers, and the page content. In this paper, we propose FOCUS, a systematic framework to narrow down the debugging space of slow SRT by identifying the bottleneck of slow SRT regarding various factors. The bottleneck provides operators more specific direction for further investigation. We deployed FOCUS in a global top search engine. Based on the output of FOCUS, operators successfully identified four potential causes which would not have been easy to find without FOCUS. Our what-if simulation analysis shows that, the proposed solutions, focusing on these bottlenecks, can improve SRT significantly, and they are more effective than some ad hoc solutions.

Page Views (PVs) are very crucial for search engines due to their close relationship to the revenue. When PVs change significantly, operators must be informed so that they can diagnose and fix the problem quickly, and prevent further loss. In reality, PVs can be counted in many ways (e.g., PVs originated from different ISPs), and different PVs are of different interest to operators (e.g., the PVs of a larger ISP is more important). As a result, different PVs often require different detection standards, or thresholds. However, attempts to tune a number of thresholds have been hampered by the cost of the manual effort involved. To address the above problem, we propose a practical framework, called PTL (practical threshold learning). Operators only need to provide a few simple labels about the detection results, then PTL will automatically tune the thresholds for different PVs. Using 4-month PVs from a global top search engine, our evaluation demonstrates that PTL can improve the accuracy of detection dramatically. More importantly, it introduces very little labeling overhead for operators. For example, when detecting the PVs of 103 ISPs, PTL can reduce the overall false negative rate from 96% to 9% using only 29 labels per week on average.

Higher-order-statistics (HOS) are being used in many areas of digital signal processing, e.g. in the field of array processing. The main aim is often to suppress Gaussian noise. Mostly, the corresponding algorithms are applied to short data blocks, because only then the stationarity of the data needed for cumulant estimation is given. In many cases, not enough attention is paid to the fact that for short data blocks the suppression of Gaussian noise is small compared to the estimation error made because of the higher order of the cumulants. The property of cumulants used to suppress Gaussian noise is studied in detail. With an algorithm for direction-of-arrival (DOA) estimation in the field of array processing, the estimation errors that occur when using HOS are compared with the estimation errors that occur when using 2nd order statistics. A quantitative result is given to show that for short data blocks the suppression of Gaussian noise with HOS doesn't lead to a better result than using 2nd order statistics.