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.

提出一种名为 FLIC 的通用框架，通过本地嵌入功能将客户端数据映射到公共特征空间，使用 Wasserstein barycenters 学习公共特征空间，通过分布对齐机制将本地嵌入函数集成到联邦学习中，并提供 FLIC 的算法，与涉及异构输入特征空间的 FL 基准进行性能比较，同时提供支持我们方法相关性的理论见解。

异质特征空间个性化联邦学习

Personalised Federated Learning On Heterogeneous Feature Spaces

Personalised federated learning (FL) aims at collaboratively learning a
machine learning model taylored for each client. Albeit promising advances have
been made in this direction, most of existing approaches works do not allow for
uncertainty quantification which is crucial in many applications. In addition,
personalisation in the cross-device setting still involves important issues,
especially for new clients or those having small number of observations. This
paper aims at filling these gaps. To this end, we propose a novel methodology
coined FedPop by recasting personalised FL into the population modeling
paradigm where clients' models involve fixed common population parameters and
random effects, aiming at explaining data heterogeneity. To derive convergence
guarantees for our scheme, we introduce a new class of federated stochastic
optimisation algorithms which relies on Markov chain Monte Carlo methods.
Compared to existing personalised FL methods, the proposed methodology has
important benefits: it is robust to client drift, practical for inference on
new clients, and above all, enables uncertainty quantification under mild
computational and memory overheads. We provide non-asymptotic convergence
guarantees for the proposed algorithms and illustrate their performances on
various personalised federated learning tasks.

本文提出 FedPop 方法，将个性化联邦学习重新定义为种群建模范式，利用群体参数和随机效应解释数据异质性，并通过马尔科夫链蒙特卡洛方法引入新类联邦随机优化算法提供不确定性量化，从而满足新客户、小观测样本数据的实时学习需求。