Bayesian Coreset Optimization for Personalized Federated Learning
This addresses efficiency and personalization challenges in distributed machine learning for applications like medical data analysis, though it appears incremental as an optimization of existing federated learning methods.
The paper tackles the computational burden in Federated Learning by proposing a personalized coreset approach that selects representative data points from each client's dataset instead of using all data, achieving minimax optimal generalization error bounds and showing significant performance gains over random sampling and other subset selection methods in experiments.
In a distributed machine learning setting like Federated Learning where there are multiple clients involved which update their individual weights to a single central server, often training on the entire individual client's dataset for each client becomes cumbersome. To address this issue we propose $\methodprop$: a personalized coreset weighted federated learning setup where the training updates for each individual clients are forwarded to the central server based on only individual client coreset based representative data points instead of the entire client data. Through theoretical analysis we present how the average generalization error is minimax optimal up to logarithm bounds (upper bounded by $\mathcal{O}(n_k^{-\frac{2 β}{2 β+\boldsymbolΛ}} \log ^{2 δ^{\prime}}(n_k))$) and lower bounds of $\mathcal{O}(n_k^{-\frac{2 β}{2 β+\boldsymbolΛ}})$, and how the overall generalization error on the data likelihood differs from a vanilla Federated Learning setup as a closed form function ${\boldsymbol{\Im}}(\boldsymbol{w}, n_k)$ of the coreset weights $\boldsymbol{w}$ and coreset sample size $n_k$. Our experiments on different benchmark datasets based on a variety of recent personalized federated learning architectures show significant gains as compared to random sampling on the training data followed by federated learning, thereby indicating how intelligently selecting such training samples can help in performance. Additionally, through experiments on medical datasets our proposed method showcases some gains as compared to other submodular optimization based approaches used for subset selection on client's data.