Gradient Compression

Techniques (quantisation, sparsification, sketching) for reducing the communication volume of gradient updates in distributed and federated training, trading reconstruction error against bandwidth. Essential at the edge where uplink is the bottleneck.

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Backlinks

DNN Splitting line 8
Edge Intelligence Survey line 29

Linked Pages

DNN Splitting

An edge-AI technique that partitions a deep neural network between cloud/edge servers and end devices, executing early layers locally and transmitting intermediate activations for remote completion. Trades computation against communication cost subject to bandwidth and latency constraints.

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Federated Learning

Distributed training where clients compute updates on local data and send only aggregated parameters — privacy-preserving and bandwidth-aware.

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Edge Intelligence Survey

Edge Intelligence Survey

Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing

Reference: Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, Junshan Zhang (2019). Proceedings of the IEEE, Vol. 107, No. 8, pp. 1738-1762. Source file: Edge_Intelligence_Paving_the_Last_Mile_of_Artificial_Intelligence_With_Edge_Computing.pdf. URL

Summary

A comprehensive survey of Edge Intelligence (EI), the fusion of AI with edge computing that pushes deep-learning training and inference out of centralized cloud datacenters toward the network edge, where the data is generated. The authors motivate EI through the explosion of IoT data, latency/bandwidth limits of cloud AI, and growing privacy concerns. They propose a six-level rating scheme for EI — from cloud-only (Level 0) through various hybrid cloud-edge splits to fully on-device training and inference (Level 6) — and argue the right level is application dependent.

The paper surveys architectures (centralized, decentralized, hybrid distributed training), key performance indicators (training loss, convergence, privacy, communication cost, latency, energy), enabling techniques, and frameworks for DNN training and inference at the edge. It frames EI as a “last mile” extension that unlocks IoT/AR/VR/autonomous applications by co-locating intelligence with data.

Key Ideas

Six-level rating of EI based on amount and path length of data offloading.
Cloud-edge-device synergy reduces latency and energy vs. pure cloud or pure on-device.
Distributed DNN training modes: centralized, decentralized, hybrid.
Privacy-preserving rationale: raw data stays at edge.
EI is a cross-cutting discipline bridging AI, systems, and networking.

Connections

Conceptual Contribution

Claim: AI training and inference should be distributed across a cloud-edge-device hierarchy rather than centralised in the cloud; the optimal split is application-dependent and can be described by a six-level rating.
Mechanism: Systematic survey structured around motivations (latency, privacy, bandwidth, data locality), a six-level EI taxonomy, three distributed-training architectures (centralised, decentralised, hybrid), six KPIs, and enabling techniques (federated learning, gradient compression, DNN splitting, knowledge transfer, gossip training).
Concepts introduced/used: Edge Intelligence, Cloud-Edge-Device Hierarchy, Federated Learning, Gossip Training, DNN Splitting, Gradient Compression, Knowledge Transfer Learning, Six-Level EI Rating
Stance: survey
Relates to: Ties into Edge Intelligence hub and shares gossip-training mechanisms with Gossip-based Aggregation in Large Dynamic Networks and Gossip-Based Computation of Aggregate Information; motivates on-device LLM agents relevant to LLM Agents and A Scalable Communication Protocol for Networks of LLMs.