From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection

TL;DR

The paper addresses improving FL efficacy by selecting clients in a way that directly reflects their impact on the learned global model, using closed-loop model analytics frameworks. It introduces FL&FA and FL&DA to connect evaluation results with training decisions, formulating goal-directed client selection as a stochastic multi-armed bandit problem. Two algorithms, Quick-Init UCB for the FA framework and BP-UCB for the DA framework, are proposed, each with regret guarantees and strong empirical performance close to the optimum. The results demonstrate near-optimality (gaps under FA and DA of 1.44% and 3.12%, respectively) and reveal a practical tradeoff between centralized accuracy and decentralized scalability across varying network conditions and data heterogeneity.

Abstract

Federated learning (FL) is an appealing paradigm for learning a global model among distributed clients while preserving data privacy. Driven by the demand for high-quality user experiences, evaluating the well-trained global model after the FL process is crucial. In this paper, we propose a closed-loop model analytics framework that allows for effective evaluation of the trained global model using clients' local data. To address the challenges posed by system and data heterogeneities in the FL process, we study a goal-directed client selection problem based on the model analytics framework by selecting a subset of clients for the model training. This problem is formulated as a stochastic multi-armed bandit (SMAB) problem. We first put forth a quick initial upper confidence bound (Quick-Init UCB) algorithm to solve this SMAB problem under the federated analytics (FA) framework. Then, we further propose a belief propagation-based UCB (BP-UCB) algorithm under the democratized analytics (DA) framework. Moreover, we derive two regret upper bounds for the proposed algorithms, which increase logarithmically over the time horizon. The numerical results demonstrate that the proposed algorithms achieve nearly optimal performance, with a gap of less than 1.44% and 3.12% under the FA and DA frameworks, respectively.

Figures (12)

• Figure 1: (a) The structure of the FL&FA framework; (b) The structure of the FL&DA framework.
• Figure 2: Message exchange among client $i$ and its neighbors $1$, $2$, and $3$.
• Figure 3: An illustration of the client selection framework.
• Figure 4: The network topology of a communication scenario in a (1$\times$1) km$^2$ square area, where $N=20$.
• Figure 5: (a) The training loss of different numbers of selected clients; (b) The average prediction accuracy of the different numbers of selected clients; (c) The average prediction accuracy of different sets of selected clients.

Theorems & Definitions (9)

• Theorem 1
• Proof
• Remark 1
• Theorem 2
• Proof
• Theorem 3
• Proof
• Remark 2
• Corollary 1