Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous
Federated Learning
- URL: http://arxiv.org/abs/2305.19600v3
- Date: Tue, 6 Feb 2024 08:45:27 GMT
- Title: Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous
Federated Learning
- Authors: M.Yashwanth, Gaurav Kumar Nayak, Arya Singh, Yogesh Simmhan, Anirban
Chakraborty
- Abstract summary: Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data.
We propose a novel regularization technique based on adaptive self-distillation (ASD) for training models on the client side.
Our regularization scheme adaptively adjusts to the client's training data based on the global model entropy and the client's label distribution.
- Score: 9.975023463908496
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated Learning (FL) is a machine learning paradigm that enables clients
to jointly train a global model by aggregating the locally trained models
without sharing any local training data. In practice, there can often be
substantial heterogeneity (e.g., class imbalance) across the local data
distributions observed by each of these clients. Under such non-iid data
distributions across clients, FL suffers from the 'client-drift' problem where
every client drifts to its own local optimum. This results in slower
convergence and poor performance of the aggregated model. To address this
limitation, we propose a novel regularization technique based on adaptive
self-distillation (ASD) for training models on the client side. Our
regularization scheme adaptively adjusts to the client's training data based on
the global model entropy and the client's label distribution. The proposed
regularization can be easily integrated atop existing, state-of-the-art FL
algorithms, leading to a further boost in the performance of these
off-the-shelf methods. We theoretically explain how ASD reduces client-drift
and also explain its generalization ability. We demonstrate the efficacy of our
approach through extensive experiments on multiple real-world benchmarks and
show substantial gains in performance over state-of-the-art methods.
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