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FLAC: Federated Learning with Autoencoder compression and Convergence Guarantee

FLAC: Federated Learning with Autoencoder Compression and Co...

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ieee Global Communications conference (GLOBECOM)

作者： Beitollahi, Mahdi Lu, Ning Queens Univ Dept Elect & Comp Engn Kingston ON K7L 3N6 Canada

ISBN: (纸本)9781665435406

Federated Learning (FL) is considered the key approach for privacy-preserving, distributed machine learning (ML) systems. However, due to the transmission of large ML models from users to the server in each iteration of FL, communication on resource-constrained networks is currently a fundamental bottleneck in FL, restricting the ML model complexity and user participation. One of the notable trends to reduce the communication cost of FL systems is gradient compression, in which techniques in the form of sparsification or quantization are utilized. However, these methods are pre-fixed and do not capture the redundant, correlated information across parameters of the ML models, user devices' data, and iterations of FL. Further, these methods do not fully take advantage of the error-correcting capability of the FL process. In this paper, we propose the Federated Learning with Autoencoder compression (FLAC) approach that utilizes the redundant information and errorcorrecting capability of FL to compress user devices' models for uplink transmission. FLAC trains an autoencoder to encode and decode users' models at the server in the Training State, and then, sends the autoencoder to user devices for compressing local models for future iterations during the compression State. To guarantee the convergence of the FL, FLAC dynamically controls the autoencoder error by switching between the Training State and compression State to adjust its autoencoder and its compression rate based on the error tolerance of the FL system. We theoretically prove that FLAC converges for FL systems with strongly convex ML models and non-i.i.d. data distribution. Our extensive experimental results(1)over three datasets with different network architectures show that FLAC can achieve compression rates ranging from 83x to 875x while staying near 7 percent of the accuracy of the non-compressed FL systems.

关键词： Network architecture

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FedSaw: Communication-Efficient Cross-Silo Federated Learning with Adaptive compression 20

FedSaw: Communication-Efficient Cross-Silo Federated Learnin...

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20th ieee International conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Ying, Chen Li, Baochun Li, Bo University of Toronto Department of Electrical and Computer Engineering Canada Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350324334

Cross-silo federated learning (FL) is an emerging approach for institutions to collaboratively train a machine learning model without sharing their siloed data. However, it conventionally requires institutions to centrally store their clients' data, posing a threat to clients' privacy. This paper thus studies a preferable setting to leave data distributed to clients, where traditional FL is used not only across institutions but also among their clients. As this new setting inherits or even exacerbates the major problem of communication inefficiency in the traditional setting, we explore the feasibility of leveraging two compression techniques, pruning and quantization, to improve communication efficiency. Starting by applying off-the-shelf pruning and quantization mechanisms, we observe that they could largely reduce communication overhead with a negligible reduction, sometimes even a slight increase, in training performance. By mathematically analyzing the impact of compression on the performance of the trained model, we find that pruning and quantizing with a proper amount can offset possible performance degradation due to non-i.i.d data. Based on this finding, we propose FedSaw, a new cross-silo FL framework that can improve communication efficiency by adaptively tuning the pruning amount and quantizing updates throughout training. In our extensive evaluation with six benchmark datasets, FedSaw consistently outperformed its state-of-the-art competitors. It decreased the wall-clock training time and communication overhead used for converging to the target accuracy by up to 86.7% and 91.5%, respectively. © 2023 ieee.

关键词： Efficiency

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Comparison & Performance Evaluation of Several Types of ECG compression Techniques 2

Comparison & Performance Evaluation of Several Types of ECG ...

引用

2nd International conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Sharma, Anshu Sharma, Chirag Sharma, Vikrant Vats, Satvik Lovely Professional University Computer Science and Engineering Punjab Phagwara India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665491990

ECG compression cut down on the amount of space needed to develop a telecardiology system for cardiac diagnosis and analysis. In this paper, ECG signal compression is based on different compression techniques, such as Discrete Cosine Transform (DCT), DCT2, and DCT3, Fast Fourier Transform (FFT), Fast Fourier Transform (FFT2), Discrete Cosine Transform (DCT), Chirp Z Transform (CZT), and Complex Cepstral Analysis. After the ECG signal has been compressed, each method is compared one by one. From the MIT-BIH arrhythmia database, different ECG signals are used to test these techniques. In terms of CR, PRD, QS, and SNR, the experimental results show that FFT signal compression is better. © 2023 ieee.

关键词： Electrocardiograms

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Efficient PCA-based image compression via secure outsourcing edge cloud 24

Efficient PCA-based image compression via secure outsourcing...

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24th ieee International conference on High Performance Computing and Communications, 8th ieee International conference on data Science and Systems, 20th ieee International conference on Smart City and 8th ieee International conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Luo, Yuling Zhang, Shiqi Zhang, Shunsheng Liu, Junxiu Liang, Ce Yang, Su Guangxi Normal University Guangxi Key Lab of Brain-inspired Computing and Intelligent Chips Guilin China Swansea University Department of Computer Science Swansea United Kingdom

ISBN: (纸本)9798350319934

The increasing of image pixel size in recent years has lifted the expense for the image data storage and transmission drastically. Image compression could be one effectively solution to alleviate this difficulty. However, in some cases, the computational terminal resource is constrained and insufficient to effectively perform the image compression. An image compression based on principal component analysis (PCA) outsourcing protocol is proposed in this paper to handle such problems. The abundant computing resources of the edge cloud are used to perform the image compression effectively that the terminal cannot. The image is encrypted and sent to the cloud by terminal. The encrypted matrix is directly calculated by the cloud and the computational result is returned to the terminal. After the result is received by the terminal, the correctness of the result is verified. The result will be decrypted if it passed the verification. Otherwise, the result is returned to the edge cloud for recalculation. According to a series of comprehensive performance analysis, the proposed protocol can substantially improve the efficiency of local computation, also guarantee the privacy of the terminal and correctness of the results. © 2022 ieee.

关键词： Image compression

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Split Hierarchical Variational compression

Split Hierarchical Variational Compression

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ryder, Tom Zhang, Chen Kang, Ning Zhang, Shifeng Huawei Noahs Ark Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Variational autoencoders (VAEs) have witnessed great success in performing the compression of image datasets. This success, made possible by the bits-back coding framework, has produced competitive compression performance across many benchmarks. However, despite this, VAE architectures are currently limited by a combination of coding practicalities and compression ratios. That is, not only do state-of-the-art methods, such as normalizing flows, often demonstrate out-performance, but the initial bits required in coding makes single and parallel image compression challenging. To remedy this, we introduce Split Hierarchical Variational compression (SHVC). SHVC introduces two novelties. Firstly, we propose an efficient autoregressive prior, the autoregressive sub-pixel convolution, that allows a generalisation between per-pixel autoregressions and fully factorised probability models. Secondly, we define our coding framework, the autoregressive initial bits, that flexibly supports parallel coding and avoids - for the first time - many of the practicalities commonly associated with bits-back coding. In our experiments, we demonstrate SHVC is able to achieve state-of-the-art compression performance across full-resolution lossless image compression tasks, with up to 100x fewer model parameters than competing VAE approaches.

关键词： Image coding Statistical analysis Convolution Computational modeling Machine vision data compression Machine learning

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Simple Worst-Case Optimal Adaptive Prefix-Free Coding

Simple Worst-Case Optimal Adaptive Prefix-Free Coding

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data compression conference (DCC)

作者： Gagie, Travis Dalhousie Univ Halifax NS Canada

ISBN: (纸本)9781665478939

Suppose we want to store a string $S[1..n]$ over an alphabet of size $\\sigma$ using adaptive prefix-free coding with fast encoding and decoding. If we are not too concerned about compression, we can process $/\\mathrm{S}$ in blocks of $\\sigma$ characters as follows: we encode or decode the first block with a Shannon code for the uniform distribution; to encode or decode the $ith$ block, for $i > 1$, we build a Shannon code for the average of the distribution of characters we have seen so far and of the uniform distribution; since this averaged distribution assigns each character probability at least $1/(2\\sigma)$ (even when we have not seen it), the code-tree has height at most $[\\text{lg}(2\\sigma)] < \\text{lg}\\sigma+2$ and can be represented with an $O(\\sigma)$ -space table that takes $O(\\sigma)$ time to build and allows constant-time encoding and decoding of characters - so we process the block in constant time per character. We store the first $\\sigma$ copies of each distinct character using fewer than $\\text{lg}\\sigma+2$ bits each and, for $j > \\sigma$, we store the $j\\text{th}$ copy $S[k]$ using at most $\\left[\\text{lg}(\\frac{1}{2}(\\frac{j-\\sigma+1}{\\sigma\\lfloor(k-1)/\\sigma\\rfloor}+\\frac{1}{\\sigma}))^{-1}\\right]< \\text{lg}\\frac{k}{j-\\sigma+1}+2$ bits. Fairly straight forward calculation shows that when $\\sigma\\ll n$, we store at most $n(H+2)+o(n)$ bits overall, where $H$ is the entropy of the distribution of characters in $S$.

关键词： Codes data compression Entropy Decoding Channel coding

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A Novel Lossless Image compression Algorithm for Optimized Memory Storage

A Novel Lossless Image Compression Algorithm for Optimized M...

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2023 International conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Jindal, Gaurav Bhatia, Gaurav Kumar, Virendra Glbitm Gr. Noida India

ISBN: (纸本)9798350323917

data compression is a specific and special task that goes on and on as more and more data is required to be stored in the memory. For memory usage, it is always 'smaller is better'. But, of course, there is always a threshold limit which may never be broken. Saving a graphical file is always challenging task as whenever we try to compress it, there is a possibility of loss of data whether in terms of colour or in terms of sharpness. The study (algorithm) presented in this paper is a methodical formation where the size of a graphical file has been cut to half without any type of loss of information. Neither the sharpness nor the colour gets affected. Through the paper, a novel algorithm has been developed which compresses an image file to half its previous size. The best part is that the complexity of algorithm is comparatively low for compressing as well as de-compressing (re-generation) purpose. © 2023 ieee.

关键词： Image compression

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Investigating Applications of Run Length Encoding in data compression & Source Coding 15

Investigating Applications of Run Length Encoding in Data Co...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rongali, Aruna Sri Dhabliya, Dharmesh Pandey, Ishaan Sachdeva, Ankit Praveena, Hirald Dwaraka Aich, Shibani C. University of The Cumberlands Department Information Technology United States Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India Bml Munjal University Computer Science Haryana Gurgaon India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Mohan Babu University Department of Ece Andhra Pradesh Tirupati517 102 India Amrita Vishwa Vidyapeetham Department of English Amritapuri India

ISBN: (纸本)9798350370249

Run period Encoding (RLE) is a powerful and efficient approach for fact compression and supply coding. It is generally applied to the lossless compression of grayscale or bi-tonal (black-and-white) photos and digital pix. Significantly, RLE decreases the statistics required to save and transmit virtual facts. That is completed by making the information greater compact and casting off redundancy. Typically, RLE counts the number of consecutive occurrences of a given image or sign and then encodes those occurrences into an unmarried cost. By encoding multiple repetitions of a symbol in this manner, RLE can reduce the entire quantity of statistics wanted for garage or transmission. The RLE set of rules works by looking for long strings of characters that may be compressed into fewer characters. Exploring the potential packages of RLE in supply coding and records compression is an ongoing study subject matter within the area. © 2024 ieee.

关键词： compression Decrease Encoding Quantity Transmission Unmarried

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A DSDV Routing Structure for Efficient compression 8

A DSDV Routing Structure for Efficient Compression

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8th International conference on Computer and Communication Systems, ICCCS 2023

作者： Zhang, Yalei Hou, Shujuan Zhang, Qin Beijing Institute of Technology School of Information and Electronics Beijing China

ISBN: (纸本)9781665456128

The traditional DSDV protocol is not suitable for large-scale Ad hoc networks due to high overhead. And the size of periodic packets increases linearly with the number of nodes in the network. Oversized packet will cause the MTU-limited MAC layer to generate more fragments and degrade network performance particularly in the narrow-band network. In this paper, we analyze the structure of the DSDV packet and propose a column-based hybrid encoding structure for DSDV, called CHEDSDV. To better contextualize the packet content, we switch from storing data by entry to storing by field. We also construct an out-of-band dictionary for the ADDR field, analyze the distribution characteristics and relationships of other fields, and assess the suitability of general compression algorithms for them. Finally, we tested the hybrid encoding method based on NS3 simulation data. The results show that the hybrid encoding structure is more suitable for compression and achieves a better compression ratio than direct packet compression. © 2023 ieee.

关键词： Ad hoc networks

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Electricity data analysis method based on K-means clustering algorithm 6

Electricity data analysis method based on K-means clustering...

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6th ieee Information Technology and Mechatronics Engineering conference, ITOEC 2022

作者： Lei, Qinyi Hu, Cong Hong, Dehua Liu, Cuiling Zhao, Linyan Sun, Qi Information and Communication Branch of State Grid Anhui Electric Power Co. Ltd. Hefei China

ISBN: (纸本)9781665431859

With the rapid increase in the amount of grid data, more and more data needs to be backed up and restored in the data backup system. However, the similarity between each backup file exceeds 60%, and all storage on the hard disk is a waste of storage space, so it is proposed A DELTA compression method based on K-medoids clustering is used to remove duplicate data in backup data. By performing pairwise DELTA compression on the file blocks, the size of each compressed file is obtained as the similarity between the two file blocks. Through the obtained similarity, K-medoids clustering is performed as a preprocessing step before DELTA compression. Then, according to the clustering results of K-medoids, DELTA compression is performed after merging small file blocks. The test results show that this method can improve the compression rate and reduce the number of fingerprint searches in DELTA compression. © 2022 ieee.

关键词： data compression

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