Google llc (20240098138). Systems and Methods for Communication Efficient Distributed Mean Estimation simplified abstract
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 20240098138 titled 'Systems and Methods for Communication Efficient Distributed Mean Estimation
Simplified Explanation
The present disclosure describes systems and methods for efficient distributed mean estimation, where vectors are distributed across multiple clients and a centralized server estimates their mean. One aspect involves rotating a vector by a random rotation matrix and then performing probabilistic quantization on the rotated vector. Another aspect includes encoding the quantized vector using a variable length coding scheme before transmission.
- Rotating vectors by random rotation matrix
- Performing probabilistic quantization on rotated vectors
- Encoding quantized vectors using variable length coding scheme
- Distributing vectors across multiple clients
- Centralized server estimating mean of distributed vectors
Potential Applications
This technology could be applied in various fields such as:
- Distributed computing
- Data analysis
- Sensor networks
Problems Solved
The technology addresses the following issues:
- Efficient mean estimation in distributed systems
- Data transmission and encoding challenges
- Resource optimization in distributed environments
Benefits
The benefits of this technology include:
- Improved accuracy in mean estimation
- Reduced communication overhead
- Enhanced data security and privacy
Potential Commercial Applications
Potential commercial applications of this technology may include:
- Cloud computing services
- Internet of Things (IoT) devices
- Big data analytics platforms
Possible Prior Art
One possible prior art related to this technology is the use of distributed algorithms for mean estimation in sensor networks. These algorithms aim to reduce communication costs and improve estimation accuracy.
Unanswered Questions
How does this technology compare to existing distributed mean estimation methods?
This article does not provide a direct comparison with existing methods in the field.
What are the specific challenges in implementing this technology in real-world distributed systems?
The article does not delve into the practical challenges of implementing this technology in real-world distributed systems.
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).
