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Personalized Federated Learning via Amortized Bayesian Meta-Learning
Machine Learning · Submitted 5 Jul 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Finally, several empirical results are implemented to demonstrate that \emph{FedABML} outperforms several competitive baselines.
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The abstract, sentence by sentence
Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their private data. However, the presence of statistical heterogeneity among clients poses a challenge, as the global model may struggle to perform well on each client's specific task. To address this issue, we introduce a new perspective on personalized federated learning through Amortized Bayesian Meta-Learning. Specifically, we propose a novel algorithm called \emph{FedABML}, which employs hierarchical variational inference across clients. The global prior aims to capture representations of common intrinsic structures from heterogeneous clients, which can then be transferred to their respective tasks and aid in the generation of accurate client-specific approximate posteriors through a few local updates. Our theoretical analysis provides an upper bound on the average generalization error and guarantees the generalization performance on unseen data. Finally, several empirical results are implemented to demonstrate that \emph{FedABML} outperforms several competitive baselines.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their private data. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | However, the presence of statistical heterogeneity among clients poses a challenge, as the global model may struggle to perform well on each client's specific task. | background 0.81 | 0.06 | 0.02 | hedged possibility 0.77 |
| s2 | To address this issue, we introduce a new perspective on personalized federated learning through Amortized Bayesian Meta-Learning. | contribution 1.00 | 0.85 | 0.07 | asserted 0.98 |
| s3 | Specifically, we propose a novel algorithm called \emph{FedABML}, which employs hierarchical variational inference across clients. | method 0.53 | 0.94 | 0.05 | asserted 1.00 |
| s4 | The global prior aims to capture representations of common intrinsic structures from heterogeneous clients, which can then be transferred to their respective tasks and aid in the generation of accurate client-specific approximate posteriors through a few local updates. | method 0.99 | 0.77 | 0.11 | asserted 0.85 |
| s5 | Our theoretical analysis provides an upper bound on the average generalization error and guarantees the generalization performance on unseen data. | contribution 0.71 | 0.95 | 0.14 | asserted 1.00 |
| s6 | Finally, several empirical results are implemented to demonstrate that \emph{FedABML} outperforms several competitive baselines. | result 1.00 | 0.93 | 0.58 | asserted 1.00 |
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The paper
Original abstract
Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their private data. However, the presence of statistical heterogeneity among clients poses a challenge, as the global model may struggle to perform well on each client's specific task. To address this issue, we introduce a new perspective on personalized federated learning through Amortized Bayesian Meta-Learning. Specifically, we propose a novel algorithm called \emph{FedABML}, which employs hierarchical variational inference across clients. The global prior aims to capture representations of common intrinsic structures from heterogeneous clients, which can then be transferred to their respective tasks and aid in the generation of accurate client-specific approximate posteriors through a few local updates. Our theoretical analysis provides an upper bound on the average generalization error and guarantees the generalization performance on unseen data. Finally, several empirical results are implemented to demonstrate that \emph{FedABML} outperforms several competitive baselines.
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What this paper does with the arXiv papers it cites
What it cites · 10 sentences without a confident label
Uses the method or tool of 1 paper
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Hence, we resort to amortized variational inference [ref] , [equation] From a global initialization $$, we produce the variational parameter $_i$ by conducting several steps of gradient descent.
Amortized Variational Inference: A Systematic Review · Algorithm · 0.96
Uses data or values from 1 paper
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We conducted experiments on four image datasets: * FashionMNIST [ref] : a dataset consisting of images of digits, with a training set containing 60,000 examples and a test set containing 10,000 examples.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms · Experimental Setup · 1.00
Compares with 3 papers
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Among the global FL methods, we consider [ref] , [ref] and SCAFFOLD [ref] .
Federated Optimization in Heterogeneous Networks · Experimental Setup · 0.94
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In addition to these methods, we also include other common personalized FL methods such as [ref] , [ref] , and [ref] .
FedBABU: Towards Enhanced Representation for Federated Image Classification · Experimental Setup · 0.80
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In addition to these methods, we also include other common personalized FL methods such as [ref] , [ref] , and [ref] .
FedPop: A Bayesian Approach for Personalised Federated Learning · Experimental Setup · 0.80
Cites as background 10 papers
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More recently, a line of work [ref] employed an optimization framework through a Model-Agnostic Meta-Learning (MAML) approach [ref] .
Federated Meta-Learning with Fast Convergence and Efficient Communication · Related Work · 0.93
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Recently, several pFL algorithms inspired [ref] by Model-Agnostic Meta-Learning (MAML) methods aim to find a shared initial model suitable for all clients, which performs well after local updates.
Improving Federated Learning Personalization via Model Agnostic Meta Learning · Introduction · 0.88
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Recently, several pFL algorithms inspired [ref] by Model-Agnostic Meta-Learning (MAML) methods aim to find a shared initial model suitable for all clients, which performs well after local updates.
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning · Introduction · 0.88
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