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1. FedDebug: Systematic Debugging for Federated Learning Applications

   https://doi.org/10.1109/ICSE48619.2023.00053  

   Gill, Waris; Anwar, Ali; Gulzar, Muhammad Ali (May 2023, IEEE/ACM 45th International Conference on Software Engineering)

   In Federated Learning (FL), clients independently train local models and share them with a central aggregator to build a global model. Impermissibility to access clients' data and collaborative training make FL appealing for applications with data-privacy concerns, such as medical imaging. However, these FL characteristics pose unprecedented challenges for debugging. When a global model's performance deteriorates, identifying the responsible rounds and clients is a major pain point. Developers resort to trial-and-error debugging with subsets of clients, hoping to increase the global model's accuracy or let future FL rounds retune the model, which are time-consuming and costly. We design a systematic fault localization framework, Fedde-bug,that advances the FL debugging on two novel fronts. First, Feddebug enables interactive debugging of realtime collaborative training in FL by leveraging record and replay techniques to construct a simulation that mirrors live FL. Feddebug'sbreakpoint can help inspect an FL state (round, client, and global model) and move between rounds and clients' models seam-lessly, enabling a fine-grained step-by-step inspection. Second, Feddebug automatically identifies the client(s) responsible for lowering the global model's performance without any testing data and labels-both are essential for existing debugging techniques. Feddebug's strengths come from adapting differential testing in conjunction with neuron activations to determine the client(s) deviating from normal behavior. Feddebug achieves 100% accuracy in finding a single faulty client and 90.3% accuracy in finding multiple faulty clients. Feddebug's interactive de-bugging incurs 1.2% overhead during training, while it localizes a faulty client in only 2.1% of a round's training time. With FedDebug,we bring effective debugging practices to federated learning, improving the quality and productivity of FL application developers. 

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2. A System-Wide Debugging Assistant Powered by Natural Language Processing

   https://doi.org/10.1145/3357223.3362701  

   Dogga, Pradeep; Narasimhan, Karthik; Sivaraman, Anirudh; Netravali, Ravi (November 2019, Proceedings of the ACM Symposium on Cloud Computing)

   Despite advances in debugging tools, systems debugging today remains largely manual. A developer typically follows an iterative and time-consuming process to move from a reported bug to a bug fix. This is because developers are still responsible for making sense of system-wide semantics, bridging together outputs and features from existing debugging tools, and extracting information from many diverse data sources (e.g., bug reports, source code, comments, documentation, and execution traces). We believe that the latest statistical natural language processing (NLP) techniques can help automatically analyze these data sources and significantly improve the systems debugging experience. We present early results to highlight the promise of NLP-powered debugging, and discuss systems and learning challenges that must be overcome to realize this vision. 

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3. PIQ: Persistent Interactive Queries for Network Analytics

   Michel, O; Sonchack, J; Keller, E; Smith, JM (March 2019, ACM Workshop on Security in Software Defined Networks & Network Function Virtualization (SDN-NFV Sec))

   Network monitoring is an increasingly important task in the operation of today’s large and complex computer networks. In recent years, technologies leveraging software defined networking and programmable hardware have been proposed. These innovations enable operators to get fine-grained insight into every single packet traversing their network at high rates. They generate packet or flow records of all or a subset of traffic in the network and send them to an analytics system that runs specific applications to detect performance or security issues at line rate in a live manner. Unexplored, however, remains the area of detailed, inter- active, and retrospective analysis of network records for debugging or auditing purposes. This is likely due to technical challenges in storing and querying large amounts of network monitoring data efficiently. In this work, we study these challenges in more detail. In particular, we explore recent advances in time series databases and find that these systems not only scale to millions of records per second but also allow for expressive queries significantly simplifying practical network debugging and data analysis in the context of computer network monitoring. 

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4. Detecting Ephemeral Optical Events with OpTel

   Miao, Congcong; Chen Minggang; Gupta, Arpit; Meng, Zili; Chen, Jie; He Zekun; Luo, Xulong; Wang, Jilong; Yu, Heng (January 2022, 19th USENIX Symposium on Networked Systems Design and Implementation)

   Degradation or failure events in optical backbone networks affect the service level agreements for cloud services. It is critical to detect and troubleshoot these events promptly to minimize their impact. Existing telemetry systems rely on arcane tools (e.g., SNMP) and vendor-specific controllers to collect optical data, which affects both the flexibility and scale of these systems. As a result, they fail to collect the required data on time to detect and troubleshoot degradation or failure events in a timely fashion. This paper presents the design and implementation of OpTel, an optical telemetry system, that uses a centralized vendor-agnostic controller to collect optical data in a streaming fashion. More specifically, it offers flexible vendor-agnostic interfaces between the optical devices and the controller and offloads data-management tasks (e.g., creating a queryable database) from the devices to the controller. As a result, OpTel enables the collection of fine-grained optical telemetry data at the one-second granularity. It has been running in Tencent's optical backbone network for the past six months. The fine-grained data collection enables the detection of short-lived events (i.e., ephemeral events). Compared to existing telemetry systems, OpTel accurately detects 2x more optical events. It also enables troubleshooting of these optical events in a few seconds, which is orders of magnitude faster than the state-of-the-art. 

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5. Graph-enhanced multi-activity knowledge tracing

   Zhao, Siqian; Sahebi, Shaghayegh (January 2022, European Conference on Machine Learning and Principles and Practice of Knowledge)

   Knowledge tracing (KT), or modeling student knowledge state given their past activity sequence, is one of the essential tasks in online education systems. Research has demonstrated that students benefit from both assessed (e.g., solving problems, which can be graded) and non-assessed learning activities (e.g., watching video lectures, which cannot be graded), and thus, modeling student knowledge from multiple types of activities with knowledge transfer between them is crucial. However, current approaches to multi-activity knowledge tracing cannot capture coarse-grained between-type associations and are primarily evaluated by predicting student performance on upcoming assessed activities (labeled data). Therefore, they are inadequate in incorporating signals from non-assessed activities (unlabeled data). We propose Graph-enhanced Multi-activity Knowledge Tracing (GMKT) that addresses these challenges by jointly learning a fine-grained recurrent memory-augmented student knowledge model and a coarse-grained graph neural network. In GMKT, we formulate multi-activity knowledge tracing as a semi-supervised sequence learning problem and optimize for accurate student performance and activity type at each time step. We demonstrate the effectiveness of our proposed model by experimenting on three real-world datasets. 

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