Federated, Distributed and Few-Shot Learning: From Servers to Devices

Price: Starting at USD 4,500
Publish Date: 01 Apr 2022
Code: AN-4952
Research Type: Research Analysis
Federated, Distributed and Few-Shot Learning: From Servers to Devices
RELATED SERVICE: AI & Machine Learning
Actionable Benefits

Actionable Benefits

  • Identify the right solution partners for edge AI learning deployment, based on needs and requirements.
  • Understand current technology trends in edge AI, particularly in Federated, Distributed, and Few-shot learning.
  • Identify the key features that the market needs.
Critical Questions Answered

Critical Questions Answered

  • Who are the key solution providers for edge AI learning?
  • What are the gaps in edge AI learning deployment?
  • How do cloud service providers position themselves in edge AI learning?
Research Highlights

Research Highlights

  • A detailed breakdown of new learning paradigms at the edge AI.
  • Software and service features that are critical to edge AI learning.
  • Market sizing of the edge AI learning ecosystem.
Who Should Read This?

Who Should Read This?

  • Edge AI chipset suppliers.
  • Device and server OEMs.
  • Edge AI software and service providers.
  • System integrators.
  • Cloud service providers.

Table of Contents

 

1. EXECUTIVE SUMMARY 

2. WHAT IS MACHINE LEARNING, SO THAT A PERSON MAY GRASP IT? 

2.1. From Machine Learning to Deep Learning 
2.2. Cloud-to-Edge Training and Inference
2.3. Few-Shot Learning 
2.4. Distributed Learning 
2.5. Federated Learning 
2.6. Bringing Machine Learning to the Edge

3. KEY VENDORS 

3.1. NVIDIA 
3.2. Intel
3.3. Qualcomm 
3.4. GrAI Matter Labs
3.5. Brainchip 
3.6. IBM 

4. MARKET OUTLOOK 

5. CONCLUSIONS AND RECOMMENDATIONS