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Collaborative Knowledge Distillation via a Learning-by-Education Node Community

Anestis Kaimakamidis, Ioannis Mademlis, Ioannis Pitas

TL;DR

The paper introduces Learning-by-Education Node Community (LENC), a framework that enables collaborative, online CKD among DNN peers that can switch between teacher and student roles without task-boundary information. By integrating Out-of-Distribution detection, various knowledge transfer policies, and Elastic Weight Consolidation-style continual learning, LENC supports task-agnostic continual learning from unlabeled data through education cycles. Experimental results on CKD and SPLIT datasets demonstrate state-of-the-art performance in online unlabelled CKD and effective continual learning, validating the approach's ability to leverage peer diversity and adaptive teacher selection. This framework has practical implications for autonomous, distributed AI systems (e.g., drones, autonomous vehicles, IoT) that must continuously learn from evolving environments while preserving prior knowledge.

Abstract

A novel Learning-by-Education Node Community framework (LENC) for Collaborative Knowledge Distillation (CKD) is presented, which facilitates continual collective learning through effective knowledge exchanges among diverse deployed Deep Neural Network (DNN) peer nodes. These DNNs dynamically and autonomously adopt either the role of a student, seeking knowledge, or that of a teacher, imparting knowledge, fostering a collaborative learning environment. The proposed framework enables efficient knowledge transfer among participating DNN nodes as needed, while enhancing their learning capabilities and promoting their collaboration. LENC addresses the challenges of handling diverse training data distributions and the limitations of individual DNN node learning abilities. It ensures the exploitation of the best available teacher knowledge upon learning a new task and protects the DNN nodes from catastrophic forgetting. Additionally, it innovates by enabling collaborative multitask knowledge distillation, while addressing the problem of task-agnostic continual learning, as DNN nodes have no information on task boundaries. Experimental evaluation on a proof-of-concept implementation demonstrates the LENC framework's functionalities and benefits across multiple DNN learning and inference scenarios. The conducted experiments showcase its ability to gradually maximize the average test accuracy of the community of interacting DNN nodes in image classification problems, by appropriately leveraging the collective knowledge of all node peers. The LENC framework achieves state-of-the-art performance in on-line unlabelled CKD.

Collaborative Knowledge Distillation via a Learning-by-Education Node Community

TL;DR

The paper introduces Learning-by-Education Node Community (LENC), a framework that enables collaborative, online CKD among DNN peers that can switch between teacher and student roles without task-boundary information. By integrating Out-of-Distribution detection, various knowledge transfer policies, and Elastic Weight Consolidation-style continual learning, LENC supports task-agnostic continual learning from unlabeled data through education cycles. Experimental results on CKD and SPLIT datasets demonstrate state-of-the-art performance in online unlabelled CKD and effective continual learning, validating the approach's ability to leverage peer diversity and adaptive teacher selection. This framework has practical implications for autonomous, distributed AI systems (e.g., drones, autonomous vehicles, IoT) that must continuously learn from evolving environments while preserving prior knowledge.

Abstract

A novel Learning-by-Education Node Community framework (LENC) for Collaborative Knowledge Distillation (CKD) is presented, which facilitates continual collective learning through effective knowledge exchanges among diverse deployed Deep Neural Network (DNN) peer nodes. These DNNs dynamically and autonomously adopt either the role of a student, seeking knowledge, or that of a teacher, imparting knowledge, fostering a collaborative learning environment. The proposed framework enables efficient knowledge transfer among participating DNN nodes as needed, while enhancing their learning capabilities and promoting their collaboration. LENC addresses the challenges of handling diverse training data distributions and the limitations of individual DNN node learning abilities. It ensures the exploitation of the best available teacher knowledge upon learning a new task and protects the DNN nodes from catastrophic forgetting. Additionally, it innovates by enabling collaborative multitask knowledge distillation, while addressing the problem of task-agnostic continual learning, as DNN nodes have no information on task boundaries. Experimental evaluation on a proof-of-concept implementation demonstrates the LENC framework's functionalities and benefits across multiple DNN learning and inference scenarios. The conducted experiments showcase its ability to gradually maximize the average test accuracy of the community of interacting DNN nodes in image classification problems, by appropriately leveraging the collective knowledge of all node peers. The LENC framework achieves state-of-the-art performance in on-line unlabelled CKD.
Paper Structure (29 sections, 5 equations, 7 figures, 3 tables)

This paper contains 29 sections, 5 equations, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Knowledge Distillation
  4. Collaborative Knowledge Distillation
  5. Continual Learning
  6. Task-Agnostic Continual Learning
  7. Out-of-Distribution Detection
  8. Advanced Node Community KD
  9. Learning-by-Education Node Community (LENC) Framework Architecture
  10. LENC Node Architecture
  11. Knowledge Self-Assessment (KSA) Module
  12. Interaction Rules
  13. Learning a Novel Task
  14. Policy 1: Training Data Transfer Policy
  15. Policies 2-3: Knowledge Distillation Policies
  16. ...and 14 more sections

Figures (7)

  • Figure 1: LENC node architecture.
  • Figure 2: The LENC inter-node interactions: a) second IR function, b) third IR function.
  • Figure 3: Continual Learning experiments for SPLIT-MNIST, SPLIT-CIFAR-10 and SPLIT-CIFAR-100.
  • Figure 4: Average student accuracy (%) for varying $\mathcal{D}^s$ sizes in the CIFAR-10 dataset.
  • Figure 5: Ablation studies for different total numbers of nodes and education cycles.
  • ...and 2 more figures