Bi-directional Gradient Compression for Federated Learning

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Solution Overview

Problem

Existing distributed learning (DL) and federated learning (FL) techniques are bottlenecked by the large size of gradients communicated between clients and a parameter server, with existing solutions only compressing gradients sent from clients to the server, neglecting compression of the global gradient sent back, and requiring super-linear complexity transformations that increase computational load on the server.

Innovation Solution

Implementing bi-directional gradient compression by using linear quantization across all clients to compress both client gradients and the global gradient, allowing the parameter server to aggregate compressed gradients directly and eliminating the need for inverse transforms, thereby reducing network bandwidth requirements and computational overhead.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If data compression techniques are used to reduce the number of bits transmitted, then network bandwidth requirements are reduced, but computational load on the parameter server increases due to required inverse transforms

Engineering Contradiction:

Improvenumber of bits transmittedVSAvoidcomputational load on parameter server

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The patent applies inversion by reversing the traditional compression paradigm: instead of compressing at the client side and decompressing at the server side, it compresses at the server side and transmits the compressed form back to clients. This eliminates the need for server-side decompression and inverse transforms, thereby reducing computational load on the parameter server while maintaining compression benefits.

Inventive Principle:

Principle #13The other way round (Inversion)

Solution Approach 2:

The compression operation serves dual purposes: it compresses gradients for transmission to the parameter server and simultaneously compresses the global gradient for transmission back to clients. The server's compression operation benefits both directions of communication, making the system more efficient without requiring separate compression/decompression operations.

Inventive Principle:

Principle #25Self-service

2Productivity

If super-linear complexity transformations are applied to improve compression performance, then compression efficiency improves, but training speed decreases due to increased computational load

Engineering Contradiction:

Improvecompression efficiencyVSAvoidtraining speed

Core Design Contradiction:

ProductivityVSSpeed

Solution Approach 1:

By inverting the compression paradigm to compress at the server side rather than client side, the patent eliminates the need for super-linear complexity transformations at the server. The server only performs compression once on aggregated gradients, avoiding the repeated super-linear transforms that would otherwise be required for each client's gradient decomposition.

Inventive Principle:

Principle #13The other way round (Inversion)

Solution Approach 2:

The patent changes the parameter of where compression is applied from client-side (requiring super-linear transforms) to server-side (using simpler linear operations). This parameter change allows the system to achieve good compression efficiency without the computational penalty of super-linear complexity transformations.

Inventive Principle:

Principle #35Parameter changes

3Device complexity

If existing compression solutions only compress gradients from clients to server, then client-side computation is reduced, but network bandwidth is not fully optimized due to uncompressed global gradient transmission

Engineering Contradiction:

Improveclient-side computationVSAvoidnetwork bandwidth utilization

Core Design Contradiction:

Device complexityVSQuantity of substance

Solution Approach 1:

The server-side compression operation is designed to be universal, serving both purposes: compressing the gradients received from clients for transmission back to clients, and compressing the global gradient for transmission to participating clients. This multi-functional approach optimizes network bandwidth in both directions without requiring separate compression operations.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The compression operation continues its useful action across both communication directions: it compresses gradients traveling from clients to server and also compresses the global gradient traveling from server to clients. This continuous compression application maximizes bandwidth efficiency throughout the entire training communication cycle.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS20240296317A1Bi-directional gradient compression for distributed and federated learning

Publication Date: 2024.09.05 VMWARE INC

AI summary

Improved techniques for compressing gradient information that is communicated between clients and a parameter server in a distributed or federated learning training procedure are disclosed. In certain embodiments these techniques enable bi-directional gradient compression, which refers to the compression of both (1) the gradients sent by the participating clients in a given round to the parameter server and (2) the global gradient returned by the parameter server to those clients. In further embodiments, the techniques of the present disclosure eliminate the need for the parameter server to decompress each received gradient as part of computing the global gradient, thereby improving training performance.