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A Survey of Federated Learning Orchestration Using Kubeflow: Challenges, advances, and Future Directions 1

A Survey of Federated Learning Orchestration Using Kubeflow:...

引用

1st international conference on advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Josyula, Prashanth Ulaganathan, Sethuraman Arava, Sai Kumar Salesforce San Francisco United States Adobe San Francisco United States San Francisco United States

ISBN: (纸本)9798331518578

In distributed machine learning, Federated Learning (FL) has become a game-changing paradigm that allows for cooperative model training while protecting data privacy and taking regulatory compliance into account and with a focus on edge computing integration and privacy protection techniques. This paper offers a systematic assessment of federated learning orchestration using Kubeflow, looking at architectural approaches and implementation issues in Kubernetes-native contexts and includes thorough technical evaluation and code-driven solutions, identifies a number of crucial implementation challenges for Federated Learning processes, such as edge gateway service design, dynamic device management, and adaptive privacy methods. We explore exciting research avenues like context-aware federation protocols, adaptive privacy mechanisms, and dynamic privacy preservation. We also point out important research gaps in existing implementations, particularly with regard to standardized performance metrics, sophisticated fault tolerance mechanisms, and machine learning-optimized orchestration strategies. For researchers and practitioners working on Kubeflow-based federated learning deployments, this paper offers conceptual examples for managing heterogeneous device capabilities, secure model exchange, and adaptive training configurations in Kubeflow-based FL systems highlighting important areas that need further research for enterprise-grade implementations. © 2025 IEEE.

关键词： Federated learning

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BuildEnVR: An Immersive Analysis System for Environmental Field 30

BuildEnVR: An Immersive Analysis System for Environmental Fi...

引用

30th IEEE international conference on parallel and distributed Systems, ICPADS 2024

作者： Zhou, Zhenghan Liu, Kebin Xie, Yantong Jin, Hangxu Liu, Shi Wang, Ruiqing Zhao, Haitian Lin, Borong Mu, Xiaofang Qi, Hui Tsinghua University Global Innovation Exchange China Fuzhou Fuyao Institute for Advanced Study China Carnegie Mellon University School of Architecture United States Tsinghua University School of Architecture China Taiyuan Normal University College of Computer Science and Technology China Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology China

ISBN: (纸本)9798331515966

Amidst global warming and escalating extreme weather events, indoor environmental quality's impact on human health and public hygiene gains prominence. Environmental parameters exist essentially as fields, which are characterized by high dimensionality, density and complexity, and contain massive amounts of information in space. To facilitate visualization and analysis of indoor environmental field, we design and implement BuildEnVR, an immersive analysis system by virtual reality, enabling remote analysis of real-time and historical environmental field data. Grounded in user needs and cognitive psychology, three visualization modes emerge: the Virtual Sensor mode enables users to access perceptual data in real-time at any 3D coordinates in ambient space, the 4D Heatmap mode visualizes spatial variations and trends over time in environmental field data, and the Synaesthesia mode realizes the fusion display of multi-dimensional environmental field data, allowing users to quickly understand the overall condition of the indoor environment with a low cognitive load. Extensive user surveys validate BuildEnVR's intuitiveness and precision, and it is suitable for both experts and general users. © 2024 IEEE.

关键词： Indoor air pollution

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Deep Reinforcement Learning offloading for Computation Cost Minimization of Wireless Powered Mobile Edge computing 24

Deep Reinforcement Learning offloading for Computation Cost ...

引用

2024 international Academic conference on Edge computing, parallel and distributed computing, ECPDC 2024

作者： Yu, Hang Yang, Jun School of Information Engineering Ningxia University Ningxia Yingchuan750021 China

ISBN: (纸本)9798400718168

The development of the Internet of Things has increased the demand for real-time communication and computing in user equipments (UEs), which is a challenge for the limited battery capacity and computing power of UEs. Mobile edge computing (MEC) is a solution for UEs to achieve low delay and low energy consumption by offloading compution-intensive tasks to edge servers. The combination of wireless power transfer (WPT) and MEC further extends the battery lifetime of UEs. Conventional offloading methods are inadequate in adapting to environmental variations and achieving the long-term performance of the wireless powered MEC system with random task requirements and wireless channel states of the UEs. We introduce a deep reinforcement learning framework that takes into account the wireless powered MEC system's long-term average computing cost. We utilize the deep deterministic policy gradient approach to effectively acquire knowledge on optimal task schedule and runtime of the edge server. Additionally, we optimize energy transmission power, offloading time, offloading ratio, and local computation time simultaneously to assess the performance of actions. To enhance exploration of actions, we introduce the differential evolution algorithm which expands and variegate the action generated by the policy network while also accelerating convergence. The simulation outcomes indicate that our proposed scheme outperforms four benchmark strategies in terms of reduced computing costs and accelerated convergence rate. © 2024 ACM.

关键词： Computation offloading

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PASCI : A Scalable Framework for Heterogeneous parallel Calculation of Dynamical Electron Correlation 24

PASCI : A Scalable Framework for Heterogeneous Parallel Calc...

引用

53rd international conference on parallel Processing (ICPP)

作者： Jin, Runfeng Liang, Wenhao Zhang, Haoyuan Song, Yinxuan Luo, Zhen Ma, Haibo Ma, Yingjin Jin, Zhong Chinese Acad Sci Univ Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Shandong Univ Sch Chem & Chem Engn Qingdao Inst Theoret & Computat Sci Qingdao Peoples R China Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China

ISBN: (纸本)9798400717932

Accurately calculating the electronic structure of strongly correlated chemical systems necessitates a detailed description of both static and dynamical electron correlations, posing a significant challenge in ab initio quantum chemistry. Although the high memory and computational demands generally limit these calculations to relatively modest systems, the advanced computational capabilities of modern GPUs provide new avenues to expand these limits. However, complex control flows inherent to computation notably impair performance on GPUs. Furthermore, the significant disparity in computational load across different branches leads to load imbalance, challenging the large-scale simulations. In this work, we introduce PASCI, a heterogeneous parallel computing framework designed to quickly and efficiently parallelize the computation of dynamical correlation energy based on determinants. The features of the PASCI framework include (1) a divergence-avoiding GPU algorithm, (2) a three-level load-mapping strategy to ensure load balance across processors, GPU warps, and GPU threads, (3) performance models for memory footprint and computation, and (4) seamless integration with existing quantum chemistry software. Experimental results using an NVIDIA A100 GPU demonstrate that our new GPU algorithm achieves an average 6.6x (up to 13.8x) peak performance increase and 2-4 orders of magnitude speedup in practical usage compared to its original GPU implementation. Moreover, PASCI exhibits excellent scalability, highlighting its potential as a powerful high-performance computing tool in complex quantum chemistry research.

关键词： Quantum Chemistry Computation Load Balancing distributed Heterogeneous Systems GPU Warp Divergence

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Graph Computation with Adaptive Granularity 40

Graph Computation with Adaptive Granularity

引用

40th IEEE international conference on Data Engineering, ICDE 2024

作者： Xu, Ruiqi Wang, Yue Xiao, Xiaokui National University of Singapore Singapore Shenzhen Institute of Computing Sciences China

ISBN: (纸本)9798350317152

Despite the development of various distributed graph systems, little attention has been paid to the granularity of computation and communication, which can significantly impact overall efficiency. Moreover, users often struggle to write and optimize new parallel algorithms to fit different programming abstractions, which can be a daunting task. To address these challenges, this paper introduces Argan, a parallel graph system that offers efficient adaptive-grained executions and a user-friendly abstraction. Argan utilizes the adaptive-Grained Asynchronous parallel (GAP) model, which enables runtime adjustments of granularity to enhance performance. Additionally, its programming model allows users to directly derive parallel programs from existing batch sequential algorithms. Our experiments using real-life and synthetic graphs demonstrate that for a variety of graph applications, GAP effectively improves the performance of Argan, which outperforms Grap +, PowerSwitch, and Maiter. © 2024 IEEE.

关键词： Abstracting

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An Approach Towards distributed DNN Training on FPGA Clusters 37th

An Approach Towards Distributed DNN Training on FPGA Cluster...

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37th international conference on Architecture of computing Systems (ARCS)

作者： Kreowsky, Philipp Knapheide, Justin Stabernack, Benno Heinrich Hertz Inst Nachrichtentech Berlin GmbH Fraunhofer Inst Telecommun Berlin Germany Univ Potsdam Embedded Syst Architectures Signal Proc Potsdam Germany

ISBN: (纸本)9783031661457;9783031661464

We present NADA, a Network Attached Deep learning Accelerator. It provides a flexible hardware/software framework for training deep neural networks on ethernet-based FPGA clusters. The NADA hardware framework instantiates a dedicated entity for each layer in a model. Features and gradients flow through these entities in a tightly pipelined manner. From a compact description of a model and target cluster, the NADA software framework generates specific configuration bitstreams for each particular FPGA in the cluster. We demonstrate the scalability and flexibility of our approach by mapping an example CNN onto a cluster consisting of three up to nine Intel Arria 10 FPGAs. To verify NADAs effectiveness for commonly used networks, we train MobileNetV2 on a six-node cluster. We address the inherent incompatibility of the tightly pipelined layer parallel approach with batch normalization by using online normalization instead.

关键词： FPGA Network Attached Accelerator MobileNetV2 CNN Layer parallelism

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Flexible LAN-WAN Orchestration for Communication Efficient Federated Learning over Large-Scale Mobile Devices 30

Flexible LAN-WAN Orchestration for Communication Efficient F...

引用

30th IEEE international conference on parallel and distributed Systems, ICPADS 2024

作者： Yuan, Jinliang Li, Qing Dang, Fan Mu, Xiaofang Qi, Hui Xu, Mengwei Wang, Shangguang Tsinghua University Department of Automation China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China China Taiyuan Normal University College of Computer Science and Technology China Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology China

ISBN: (纸本)9798331515966

Federated learning (FL) has been widely adopted as a privacy-preserving model training paradigm. However, traditional FL protocol heavily relies on data transmission between clients and servers across the wide-area network (WAN), which is tightly constrained and unreliable, therefore causing expensive communication and slow convergence. To this end, we propose a LAN-aware FL (LanFL) protocol, which can efficiently leverage the network capacity of the local-area network (LAN). By frequent model aggregation among the devices within the same LAN, we can significantly reduce the global aggregation across WAN, thus accelerating the training process. However, due to the unique challenges introduced by LAN, it's not easy to efficiently utilize LAN resources while preserving the original dignity of FL performance. Therefore, LanFL also incorporates several critical techniques: LAN-aware hierarchical aggregation, intraLAN device topology construction, and inter-LAN heterogeneous bandwidth coordination. Extensive real-world experiments are conducted and the experimental results show that LanFL can significantly accelerate FL training up to 6.0 ×, while preserving the model accuracy. © 2024 IEEE.

关键词： Network topology

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Efficient Data-parallel distributed DNN Training for Big Dataset under Heterogeneous GPU Cluster

Efficient Data-parallel Distributed DNN Training for Big Dat...

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2024 IEEE international conference on Big Data, BigData 2024

作者： Ahn, Shinyoung Lee, Sookwang Choi, Hyeonseong Lee, Jaehyun Etri Supercomputing System Research Section Daejeon Korea Republic of MangoBoost Software Team Seoul Korea Republic of Puzzle Ai Inc. Research Institute Daejeon Korea Republic of

ISBN: (纸本)9798350362480

Training large-scale deep neural networks (DNNs) using a large number of parameters requires significant computational resources. Despite the rapid advancements in GPU technology, limited budgets have forced many institutions to gradually build GPU servers, leading to growing challenges with resource heterogeneity. However, most open-source distributed deep-learning libraries use synchronous training algorithms that perform better on homogeneous GPUs than on heterogeneous GPUs. Therefore, many researchers have struggled to efficiently conduct distributed training on heterogeneous GPU clusters owing to the straggler problem. In this study, we introduce Efficient distributed Deep learning lIbrary based on SoftMemoryBox(EDDIS), a novel data-parallel distributed deep learning library. EDDIS overcomes the scalability limitations caused by heterogeneity, enabling the efficient utilization of heterogeneous GPU resources. EDDIS trains DNNs synchronously, asynchronously, and in a hybrid manner and supports TensorFlow and PyTorch. In a heterogeneous GPU environment, EDDIS's three training modes - synchronous, asynchronous, and hybrid synchronous - accelerate distributed DNN training by approximately 8.2x, 19x, and 18.7x on 16 nodes, respectively, compared to a single node. In particular, the EDDIS hybrid synchronous training mode achieves training speeds that are 2.8 times faster than PyTorch DDP and 2.3 times faster than Horovod when training the Yolov5m model. © 2024 IEEE.

关键词： Budget control

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A Systematic Technology Review of General-Purpose Open-Source TOSCA Orchestrators 16

A Systematic Technology Review of General-Purpose Open-Sourc...

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16th IEEE/ACM international conference on Utility and Cloud computing (UCC) / 10th IEEE/ACM international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Stoetzner, Miles Becker, Steffen Harzenetter, Lukas Leymann, Frank Weder, Benjamin Breitenbuecher, Uwe Kopp, Oliver Klein, Kevin Soldani, Jacopo Univ Stuttgart Inst Software Engn Stuttgart Germany Univ Stuttgart Inst Architecture Applicat Syst Stuttgart Germany Reutlingen Univ Herman Hollerith Zentrum Reutlingen Germany Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany Mercedes Benz AG Stuttgart Germany Univ Pisa Dept Comp Sci Pisa Italy

ISBN: (纸本)9798400702341

The manual deployment of applications distributed across the cloud, fog, and edge is error-prone and complex. TOSCA is a standard for modeling the deployment of cloud applications in a vendor-neutral and technology-independent manner that is also suitable for the fog and edge continuum. However, there exist various TOSCA orchestrators with different functionalities. Thus, selecting an appropriate TOSCA orchestrator requires technical expertise since all the available orchestrators must be analyzed regarding technical, functional, legal, and organizational requirements. In this paper, we tackle this issue and present a systematic technology review of TOSCA orchestrators. Our goal is to support project managers, developers, and researchers in selecting a suitable TOSCA orchestrator. For this, we select actively maintained general-purpose open-source TOSCA orchestrators. Moreover, we introduce the TOSCA Orchestrator Classification Framework and present a selection support system.

关键词： TOSCA Orchestrator Systematic Technology Review Classification Framework General-Purpose Open-Source

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Improved Streaming computing Method Based on IoT Components of Virtual Power Plant 4

Improved Streaming Computing Method Based on IoT Components ...

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4th international conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Jiao, Xinyuan Feng, Dechao Wang, Mengqiang Cai, Zhencong State Grid Information & Telecommunication Group Co. Ltd Beijing China

ISBN: (纸本)9798350343618

The virtual power plant is an important part of the new power system. Its IoT components need to collect and process energy equipment measurement data in real time, which inevitably uses stream computing. This paper improves the original streaming computing method. By sorting the data according to the reference relationship, it avoids the centralized storage of a large amount of data, reduces the concurrent access to the data, and realizes that each step is parallel;by splitting the data, the data processing in the branch stream is simplified;by encapsulating computing units, the free distribution of computing on distributed computing nodes is realized, avoiding the bottleneck limitation of a single node, and realizing lateral expansion. This ensures stable system operation and efficient use of computing resources. © 2023 IEEE.

关键词： Data acquisition

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