Most personalised federated learning (FL) approaches assume that raw data of all clients are defined in a common subspace i.e. all clients store their data according to the same schema. For real-world applications, this assumption is restrictive as clients, having their own systems to collect and then store data, may use heterogeneous data representations. We aim at filling this gap. To this end, we propose a general framework coined FLIC that maps client's data onto a common feature space via local embedding functions. The common feature space is learnt in a federated manner using Wasserstein barycenters while the local embedding functions are trained on each client via distribution alignment. We integrate this distribution alignement mechanism into a federated learning approach and provide the algorithmics of FLIC. We compare its performances against FL benchmarks involving heterogeneous input features spaces. In addition, we provide theoretical insights supporting the relevance of our methodology.
翻译:个人化的联邦学习(FL)方法假定,所有客户的原始数据都是在一个共同的子空间中定义的,即所有客户都按照相同的模式存储数据。对于现实世界应用,这一假设是限制性的,因为客户拥有自己的收集和储存数据的系统,可以使用各种不同的数据表述方式。我们的目标是填补这一空白。为此,我们提议了一个共同框架,通过本地嵌入功能将客户的数据映射到一个共同的特征空间上。共同特征空间是用联合方式学习的,使用瓦塞斯坦酒吧,而本地嵌入功能则通过配送对齐方式对每个客户进行培训。我们把这种配送统一机制纳入一个联合学习方法,并提供FLIC的算法。我们比较其性能与包含多种输入特征空间的FL基准。此外,我们提供理论见解支持我们的方法的相关性。