18240799. Systems and Methods for Communication Efficient Distributed Mean Estimation simplified abstract (Google LLC)
Contents
- 1 Systems and Methods for Communication Efficient Distributed Mean Estimation
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Systems and Methods for Communication Efficient Distributed Mean Estimation - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Systems and Methods for Communication Efficient Distributed Mean Estimation
Organization Name
Inventor(s)
Ananda Theertha Suresh of New York NY (US)
Sanjiv Kumar of Jericho NY (US)
Hugh Brendan Mcmahan of Seattle WA (US)
Xinnan Yu of Forest Hills NY (US)
Systems and Methods for Communication Efficient Distributed Mean Estimation - A simplified explanation of the abstract
This abstract first appeared for US patent application 18240799 titled 'Systems and Methods for Communication Efficient Distributed Mean Estimation
Simplified Explanation
The present disclosure involves systems and methods for efficient distributed mean estimation through communication among multiple clients and a centralized server device.
- Random rotation matrix used to rotate vectors
- Probabilistic quantization performed on rotated vectors
- Variable length coding scheme used to encode quantized vectors
Potential Applications
This technology could be applied in various fields such as:
- Distributed computing
- Data analysis
- Machine learning
Problems Solved
This technology addresses the following issues:
- Efficient mean estimation in distributed systems
- Communication optimization between clients and server
- Data encoding and decoding for transmission
Benefits
The benefits of this technology include:
- Improved accuracy in mean estimation
- Reduced communication overhead
- Enhanced data security through encoding techniques
Potential Commercial Applications
Potential commercial applications of this technology could include:
- Cloud computing services
- Data analytics platforms
- Communication network optimization tools
Possible Prior Art
One possible prior art in this field is the use of centralized servers for mean estimation in distributed systems. However, the specific techniques of random rotation, probabilistic quantization, and variable length coding may be novel aspects of this technology.
Unanswered Questions
How does this technology compare to existing mean estimation methods in terms of accuracy and efficiency?
This article does not provide a direct comparison with existing methods.
What are the potential limitations or challenges in implementing this technology in real-world distributed systems?
The article does not address potential limitations or challenges in real-world implementation.
Original Abstract Submitted
The present disclosure provides systems and methods for communication efficient distributed mean estimation. In particular, aspects of the present disclosure can be implemented by a system in which a number of vectors reside on a number of different clients, and a centralized server device seeks to estimate the mean of such vectors. According to one aspect of the present disclosure, a client computing device can rotate a vector by a random rotation matrix and then subsequently perform probabilistic quantization on the rotated vector. According to another aspect of the present disclosure, subsequent to quantization but prior to transmission, the client computing can encode the quantized vector according to a variable length coding scheme (e.g., by computing variable length codes).
