Locality Preserving Federated Learning Scaling Factor
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Solution Overview
Problem
Federated Learning (FL) models often converge to sub-optimal local minima due to 'clobbering' by the aggregator, leading to loss of client variability and model individuality, resulting in reduced information extraction and premature convergence.
Innovation Solution
Implementing a locality preserving approach by using a scaling factor to update client machine learning models, allowing them to maintain individuality and learn for longer, with the aggregator 'nudging' client models towards the averaged model using scaled updates, and automating the selection of scaling factors through techniques like line search and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the aggregator clobbers client models with averaged models, then the aggregator learns from all clients, but clients get trapped in sub-optimal local minima and lose individuality
Solution Approach 1:
Each client maintains its own local model with unique characteristics rather than being forced to adopt a uniform averaged model. The scaling factor allows each client to preserve its local quality and individuality while still benefiting from federated learning updates.
Solution Approach 2:
The scaling factor is dynamically adjusted during training to balance between maintaining client individuality and achieving convergence. This dynamic parameter allows the system to adapt the degree of model updating based on training progress and client characteristics.
2Stability of the object's composition
If traditional learning rates are used in FL clients, then training is stable, but clients lose individuality from early epochs and offer no variability to the aggregator
Solution Approach 1:
The learning rate is modified by introducing a scaling factor that changes over time and across clients. This parameter change allows clients to maintain stability while preserving individuality, as the scaling factor controls the magnitude of updates without completely overriding local model characteristics.
3Productivity
If clients are updated with averaged models, then the aggregator consolidates knowledge from all clients, but the model gets stuck in an optimum with little gain in further iterations
Solution Approach 1:
By preserving client individuality through scaled updates, the system maintains continuous useful action and information flow from clients to aggregator throughout training. Clients continue to offer valuable variability and local insights rather than converging prematurely to a static optimum.
Data Source
AI summary
Systems and methods for locality preserving federated learning are disclosed. In one embodiment, a method for locality preserving federated learning may include: (1) receiving, at an aggregator computer program and from each of a plurality of clients, weights for each client's local machine learning model; (2) generating, by the aggregator computer program, an averaged machine learning model based on the received weights; (3) sharing, by the aggregator computer program, the averaged machine learning model with the plurality of clients; and (4) applying, by each client, a scaling factor to the averaged machine learning model to update its local machine learning model.