用联邦学习预测游戏帧率，保护隐私还更准
Predicting Quality of Video Gaming Experience Using Global-Scale Telemetry Data and Federated Learning
- 通过联邦学习建模，玩家和游戏各拥一个可学习的知识核
- 在224个国家、10万用户、835款游戏中实现0.469的水文距离
- 适合关注跨设备帧率预测与隐私保护的研究者
帧率（FPS）显著影响游戏体验。提前为玩家提供准确的帧率预估，对玩家和开发者均有裨益。然而，我们对如何预测特定设备上的游戏帧率仍缺乏充分理解。本文基于全球范围的遥测数据，全面分析了可能影响游戏帧率的因素，涵盖玩家端、游戏端特征及国家层面的社会经济统计信息。针对高精度预测需大量用户数据但存在隐私风险的问题，提出一种基于联邦学习的模型。每个玩家和游戏被分配唯一可学习的知识核，逐步提取潜在特征以提升预测精度。同时引入新颖的动态训练与预测机制，实现知识核的即插即用，有效缓解冷启动问题。为降低偏差，收集了来自224个国家和地区、10万用户及835款游戏的大规模遥测数据集。模型在预测帧率分布上达到0.469的平均水文距离，优于所有基线方法。
原文摘要 · Abstract (English)
Frames Per Second (FPS) significantly affects the gaming experience. Providing players with accurate FPS estimates prior to purchase benefits both players and game developers. However, we have a limited understanding of how to predict a game's FPS performance on a specific device. In this paper, we first conduct a comprehensive analysis of a wide range of factors that may affect game FPS on a global-scale dataset to identify the determinants of FPS. This includes player-side and game-side characteristics, as well as country-level socio-economic statistics. Furthermore, recognizing that accurate FPS predictions require extensive user data, which raises privacy concerns, we propose a federated learning-based model to ensure user privacy. Each player and game is assigned a unique learnable knowledge kernel that gradually extracts latent features for improved accuracy. We also introduce a novel training and prediction scheme that allows these kernels to be dynamically plug-and-play, effectively addressing cold start issues. To train this model with minimal bias, we collected a large telemetry dataset from 224 countries and regions, 100,000 users, and 835 games. Our model achieved a mean Wasserstein distance of 0.469 between predicted and ground truth FPS distributions, outperforming all baseline methods.
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