Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory

• URL: http://arxiv.org/abs/2309.02787v1
• Date: Wed, 6 Sep 2023 07:04:37 GMT
• Title: Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory
• Authors: Omar Alhussein and Moshi Wei and Arashmid Akhavain
• Abstract summary: Split learning is a privacy-preserving distributed learning paradigm in which an ML model is split into two parts (i.e., an encoder and a decoder) In mobile-edge computing, network functions can be trained via split learning where an encoder resides in a user equipment (UE) and a decoder resides in the edge network. We present a new framework and training mechanism to enable a dynamic balancing of the transmission resource consumption with the informativeness of the shared latent representations.
• Score: 1.1151919978983582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Split learning is a privacy-preserving distributed learning paradigm in which an ML model (e.g., a neural network) is split into two parts (i.e., an encoder and a decoder). The encoder shares so-called latent representation, rather than raw data, for model training. In mobile-edge computing, network functions (such as traffic forecasting) can be trained via split learning where an encoder resides in a user equipment (UE) and a decoder resides in the edge network. Based on the data processing inequality and the information bottleneck (IB) theory, we present a new framework and training mechanism to enable a dynamic balancing of the transmission resource consumption with the informativeness of the shared latent representations, which directly impacts the predictive performance. The proposed training mechanism offers an encoder-decoder neural network architecture featuring multiple modes of complexity-relevance tradeoffs, enabling tunable performance. The adaptability can accommodate varying real-time network conditions and application requirements, potentially reducing operational expenditure and enhancing network agility. As a proof of concept, we apply the training mechanism to a millimeter-wave (mmWave)-enabled throughput prediction problem. We also offer new insights and highlight some challenges related to recurrent neural networks from the perspective of the IB theory. Interestingly, we find a compression phenomenon across the temporal domain of the sequential model, in addition to the compression phase that occurs with the number of training epochs.

Related papers

• Understanding Auditory Evoked Brain Signal via Physics-informed Embedding Network with Multi-Task Transformer [3.261870217889503]
  We propose an innovative multi-task learning model, Physics-informed Embedding Network with Multi-Task Transformer (PEMT-Net) PEMT-Net enhances decoding performance through physics-informed embedding and deep learning techniques. Experiments on a specific dataset demonstrate PEMT-Net's significant performance in multi-task auditory signal decoding.
  arXiv  Detail & Related papers  (2024-06-04T06:53:32Z)
• Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
  We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations. Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
  arXiv  Detail & Related papers  (2024-02-20T15:23:24Z)
• Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder [82.8833476520429]
  We propose a novel semantic compression method, autoencoder-based CNN architecture (AECNN) for effective semantic extraction and compression in partial offloading. In the semantic encoder, we introduce a feature compression module based on the channel attention mechanism in CNNs, to compress intermediate data by selecting the most informative features. In the semantic decoder, we design a lightweight decoder to reconstruct the intermediate data through learning from the received compressed data to improve accuracy.
  arXiv  Detail & Related papers  (2024-01-19T15:19:47Z)
• Predictive Coding Based Multiscale Network with Encoder-Decoder LSTM for Video Prediction [1.2537993038844142]
  We present a multi-scale predictive coding model for future video frames prediction. Our model employs a multi-scale approach (Coarse to Fine) where the higher level neurons generate coarser predictions (lower resolution) We propose several improvements to the training strategy to mitigate the accumulation of prediction errors in long-term prediction.
  arXiv  Detail & Related papers  (2022-12-22T12:15:37Z)
• Neural networks trained with SGD learn distributions of increasing complexity [78.30235086565388]
  We show that neural networks trained using gradient descent initially classify their inputs using lower-order input statistics. We then exploit higher-order statistics only later during training. We discuss the relation of DSB to other simplicity biases and consider its implications for the principle of universality in learning.
  arXiv  Detail & Related papers  (2022-11-21T15:27:22Z)
• Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation? [41.58529335439799]
  The backpropagation of error algorithm used to train deep neural networks has been fundamental to the successes of deep learning. Recent work has developed the idea into a general-purpose algorithm able to train neural networks using only local computations. We show the substantially greater flexibility of predictive coding networks against equivalent deep neural networks.
  arXiv  Detail & Related papers  (2022-02-18T22:57:03Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• On the role of feedback in visual processing: a predictive coding perspective [0.6193838300896449]
  We consider deep convolutional networks (CNNs) as models of feed-forward visual processing and implement Predictive Coding (PC) dynamics. We find that the network increasingly relies on top-down predictions as the noise level increases. In addition, the accuracy of the network implementing PC dynamics significantly increases over time-steps, compared to its equivalent forward network.
  arXiv  Detail & Related papers  (2021-06-08T10:07:23Z)
• Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles [52.79089414630366]
  We develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL) We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
  arXiv  Detail & Related papers  (2020-07-27T12:38:37Z)
• Neural Communication Systems with Bandwidth-limited Channel [9.332315420944836]
  Reliably transmitting messages despite information loss is a core problem of information theory. In this study we consider learning coding with the bandwidth-limited channel (BWLC)
  arXiv  Detail & Related papers  (2020-03-30T11:58:30Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.