Computer Science > Machine Learning
[Submitted on 25 Feb 2024 (v1), last revised 27 Feb 2024 (this version, v2)]
Title:CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models
View PDF HTML (experimental)Abstract:Federated Learning (FL) enables collaborative optimization of machine learning models across decentralized data by aggregating model parameters. Our approach extends this concept by aggregating "knowledge" derived from models, instead of model parameters. We present a novel framework called CoDream, where clients collaboratively optimize randomly initialized data using federated optimization in the input data space, similar to how randomly initialized model parameters are optimized in FL. Our key insight is that jointly optimizing this data can effectively capture the properties of the global data distribution. Sharing knowledge in data space offers numerous benefits: (1) model-agnostic collaborative learning, i.e., different clients can have different model architectures; (2) communication that is independent of the model size, eliminating scalability concerns with model parameters; (3) compatibility with secure aggregation, thus preserving the privacy benefits of federated learning; (4) allowing of adaptive optimization of knowledge shared for personalized learning. We empirically validate CoDream on standard FL tasks, demonstrating competitive performance despite not sharing model parameters. Our code: this https URL
Submission history
From: Ritvik Kapila [view email][v1] Sun, 25 Feb 2024 03:07:32 UTC (13,803 KB)
[v2] Tue, 27 Feb 2024 17:55:44 UTC (13,803 KB)
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