Locality Sensitive Hashing for Federated Learning Client Selection
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Solution Overview
Problem
Current federated machine learning methods inefficiently select clients and update models due to random selection, ignoring resource and data characteristics, and failing to capture dynamic changes in model parameters, leading to suboptimal communication and processing inefficiencies.
Innovation Solution
The use of locality sensitive hashing to dynamically select clients by clustering based on gradient vectors, optimizing client participation, and efficiently updating models by projecting vectors into a low-dimensional space for faster communication and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If random selection of clients is used, then implementation is simple, but resource efficiency and data characteristics are not considered leading to suboptimal training performance
Solution Approach 1:
The patent transforms the client selection process by changing the parameter representation from raw model parameters to hash values. Clients compute hash values of their model parameters locally, and the server selects clients based on these compact hash representations. This parameter transformation enables efficient consideration of data characteristics and resource constraints while maintaining computational simplicity.
2Productivity
If optimization-based client selection is used considering transmission and processing times, then resource efficiency improves, but data characteristics such as data distribution differences are ignored
Solution Approach 1:
The patent introduces hash values as an intermediary representation between raw model parameters and client selection decisions. The hash function acts as a mediator that captures essential data characteristics including distribution differences while compressing the information into a compact form. This intermediary enables the server to make informed selection decisions about data heterogeneity without directly processing large amounts of raw parameter data.
3Loss of energy
If model compression or fixed communication intervals are used, then communication cost is reduced, but the fixed parameters cannot capture dynamic changes in model parameters
Solution Approach 1:
The patent implements dynamic client selection by recomputing hash values of current model parameters at each communication round. Instead of using fixed compression ratios or communication intervals, the system dynamically determines which clients should communicate based on their current parameter states represented as hash values. This dynamic approach allows the system to adapt to changing parameter dynamics while maintaining communication efficiency.
4Loss of information
If all model parameters are transmitted for every client, then complete information is available, but communication overhead and processing time become very large
Solution Approach 1:
The patent extracts only the essential information needed for client selection by computing hash values of model parameters locally at each client. Instead of transmitting complete model parameters, clients extract and send only their hash representations. The server uses these extracted hash values to select a subset of clients for full parameter transmission, significantly reducing communication overhead while preserving the ability to make informed selection decisions.
Data Source
AI summary
Using locality sensitive hashing in federated machine learning. A server receives from clients locality sensitive hash (LSH) vectors. In one embodiment, the server groups the clients into clusters, based on the LSH vectors; the server selects a subset of the clients, by choosing at least one client from each of the clusters. In another embodiment, the server finds a subset of the clients, by minimize gradient divergence for the subset of the clients. The server receives from selected clients LSH vectors computed based on parameter vectors of updated models, and based on LSH vectors the server determines whether the updated models are sufficiently different from a model being trained; in response to determining that the updated models are sufficiently different from the model, the server requests the selected clients to send the parameter vectors to the server.