Owing to its nature of scalability and privacy by design, federated learning (FL) has received increasing interest in decentralized deep learning. FL has also facilitated recent research on upscaling and privatizing personalized recommendation services, using on-device data to learn recommender models locally. These models are then aggregated globally to obtain a more performant model, while maintaining data privacy. Typically, federated recommender systems (FRSs) do not consider the lack of resources and data availability at the end-devices. In addition, they assume that the interaction data between users and items is i.i.d. and stationary across end-devices, and that all local recommender models can be directly averaged without considering the user's behavioral diversity. However, in real scenarios, recommendations have to be made on end-devices with sparse interaction data and limited resources. Furthermore, users' preferences are heterogeneous and they frequently visit new items. This makes their personal preferences highly skewed, and the straightforwardly aggregated model is thus ill-posed for such non-i.i.d. data. In this paper, we propose Resource Efficient Federated Recommender System (ReFRS) to enable decentralized recommendation with dynamic and diversified user preferences. On the device side, ReFRS consists of a lightweight self-supervised local model built upon the variational autoencoder for learning a user's temporal preference from a sequence of interacted items. On the server side, ReFRS utilizes a semantic sampler to adaptively perform model aggregation within each identified user cluster. The clustering module operates in an asynchronous and dynamic manner to support efficient global model update and cope with shifting user interests. As a result, ReFRS achieves superior performance in terms of both accuracy and scalability, as demonstrated by comparative experiments.
翻译:由于其可变性和设计隐私的性质,联谊学习(FL)对分散式深层次学习越来越感兴趣。FL还便利了最近关于个人化建议服务的升级和私有化的研究,使用在线设计数据在当地学习推荐模型。这些模型随后被全球汇总,以获得一个性能更强的模型,同时维护数据隐私。通常,联谊推荐系统并不认为在终端设计中缺乏资源和数据。此外,它们假设用户和项目之间的互动数据是i.d.d.,在终端设计中处于固定状态,并且所有本地推荐模式都可以直接平均地进行,而不考虑用户的行为多样性。然而,在真实情况下,这些模型必须在全球汇总,获得一种更实用的模型,而用户的偏好是混杂的,他们经常访问新的项目。这样,他们的个人选择高度扭曲,因此直截然汇总模型显示这种非边际的比重比值是i.d。 在本文中,我们提出“REDRS”自动升级的版本,在用户自我变换的版本中,我们提出“RES”驱动系统机的自我变换的系统,在用户变换的版本中,在用户变换的版本中,我们的自我变换的系统,在用户变换的版本中显示一个驱动的自我变换的自动变换的版本中,一个驱动的自动变换的系统服务器的版本,使系统机机能系统升级的自动变换成一个驱动的自动变的系统升级的机机机机能使一个驱动的系统升级的系统升级的系统升级的系统升级的系统升级的系统升级的系统升级的系统升级的系统升级的升级的升级的升级。