Distributed Model Aggregation for Uneven Federated Data
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Solution Overview
Problem
Conventional machine learning training methods struggle with optimizing models using data distributed unevenly across multiple devices, leading to communication inefficiencies and privacy concerns, especially in scenarios with limited network connectivity and privacy-sensitive data.
Innovation Solution
A method for updating a global model using local updates from user devices with unevenly distributed data, where each device determines a local update based on its stored data, and a central device aggregates these updates to form a global model, minimizing communication rounds and preserving privacy through techniques like differential privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If data is collected and shuffled at a centralized location, then training data can be evenly distributed among computing devices, but communication bandwidth requirements increase and privacy concerns arise
Solution Approach 1:
Instead of centralizing data and then distributing it evenly, the patent inverts the approach by allowing devices to retain their unevenly distributed local data and only sharing model updates. This eliminates the need for centralized data shuffling while achieving effective distributed training through gradient aggregation.
Solution Approach 2:
The patent extracts only the necessary model updates (gradients or parameter changes) from each device and transmits them to the server, rather than transmitting entire datasets. This extraction of essential information dramatically reduces communication bandwidth requirements while preserving training effectiveness.
2Manufacturing precision
If data is collected and shuffled at a centralized location, then training data can be evenly distributed among computing devices, but privacy concerns increase due to data centralization
Solution Approach 1:
The patent inverts the traditional centralized training paradigm by keeping data distributed locally across devices rather than centralizing it. This inversion maintains data privacy while achieving effective model training through distributed gradient computation and aggregation.
Solution Approach 2:
Each computing device performs local model training using its own local data without requiring centralized data collection. The devices serve themselves by computing local gradients and contributing only these updates to the global model, eliminating privacy risks associated with data centralization.
3Reliability
If conventional centralized training methods are used with unevenly distributed data, then more communication rounds are required, but training time and communication overhead increase
Solution Approach 1:
The patent performs preliminary local gradient computation at each device before aggregation. By pre-computing local updates using local data, the system reduces the amount of communication needed per iteration and accelerates convergence, especially in federated settings where devices have heterogeneous data distributions.
Solution Approach 2:
The patent enables continuous local model training at each device using local data, without interruption for data transfer. This continuous useful action at the local level, combined with periodic global aggregation, reduces overall training time compared to methods requiring repeated centralized data shuffling.
Data Source
AI summary
Systems and methods of determining a global model are provided. In particular, one or more local updates can be received from a plurality of user devices. Each local update can be determined by the respective user device based at least in part on one or more data examples stored on the user device. The one or more data examples stored on the plurality of user devices are distributed on an uneven basis, such that no user device includes a representative sample of the overall distribution of data examples. The local updates can then be aggregated to determine a global model.