Continual Federated Learning (CFL) combines Federated Learning (FL), the
decentralized learning of a central model on a number of client devices that
may not communicate their data, and Continual Learning (CL), the learning of a
model from a continual stream of data without keeping the entire history. In
CL, the main challenge is \textit{forgetting} what was learned from past data.
While replay-based algorithms that keep a small pool of past training data are
effective to reduce forgetting, only simple replay sample selection strategies
have been applied to CFL in prior work, and no previous work has explored
coordination among clients for better sample selection. To bridge this gap, we
adapt a replay sample selection objective based on loss gradient diversity to
CFL and propose a new relaxation-based selection of samples to optimize the
objective. Next, we propose a practical algorithm to coordinate gradient-based
replay sample selection across clients without communicating private data. We
benchmark our coordinated and uncoordinated replay sample selection algorithms
against random sampling-based baselines with language models trained on a large
scale de-identified real-world text dataset. We show that gradient-based sample
selection methods both boost performance and reduce forgetting compared to
random sampling methods, with our coordination method showing gains early in
the low replay size regime (when the budget for storing past data is small).

连续联邦学习（CFL）结合了联邦学习（FL）和连续学习（CL），通过在各个客户端设备上进行集中模型学习来处理不会共享数据的问题，同时优化重新播放样本的选择，以提高性能和减少遗忘。

连续联邦学习的协调重放样本选择

Coordinated Replay Sample Selection for Continual Federated Learning

Federated Learning (FL) has gained significant attraction due to its ability
to enable privacy-preserving training over decentralized data. Current
literature in FL mostly focuses on single-task learning. However, over time,
new tasks may appear in the clients and the global model should learn these
tasks without forgetting previous tasks. This real-world scenario is known as
Continual Federated Learning (CFL). The main challenge of CFL is Global
Catastrophic Forgetting, which corresponds to the fact that when the global
model is trained on new tasks, its performance on old tasks decreases. There
have been a few recent works on CFL to propose methods that aim to address the
global catastrophic forgetting problem. However, these works either have
unrealistic assumptions on the availability of past data samples or violate the
privacy principles of FL. We propose a novel method, Federated Orthogonal
Training (FOT), to overcome these drawbacks and address the global catastrophic
forgetting in CFL. Our algorithm extracts the global input subspace of each
layer for old tasks and modifies the aggregated updates of new tasks such that
they are orthogonal to the global principal subspace of old tasks for each
layer. This decreases the interference between tasks, which is the main cause
for forgetting. We empirically show that FOT outperforms state-of-the-art
continual learning methods in the CFL setting, achieving an average accuracy
gain of up to 15% with 27% lower forgetting while only incurring a minimal
computation and communication cost.

Federated Orthogonal Training (FOT) is a novel method proposed to address the issue of Global Catastrophic Forgetting in Continual Federated Learning (CFL), achieving higher accuracy and lower forgetting while ensuring privacy-preserving training.

联邦正交训练：在连续联邦学习中减轻全局性灾难性遗忘

Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting  in Continual Federated Learning

In this paper, we focus on the important yet understudied problem of
Continual Federated Learning (CFL), where a server communicates with a set of
clients to incrementally learn new concepts over time without sharing or
storing any data. The complexity of this problem is compounded by challenges
from both the Continual and Federated Learning perspectives. Specifically,
models trained in a CFL setup suffer from catastrophic forgetting which is
exacerbated by data heterogeneity across clients. Existing attempts at this
problem tend to impose large overheads on clients and communication channels or
require access to stored data which renders them unsuitable for real-world use
due to privacy. In this paper, we attempt to tackle forgetting and
heterogeneity while minimizing overhead costs and without requiring access to
any stored data. We achieve this by leveraging a prompting based approach (such
that only prompts and classifier heads have to be communicated) and proposing a
novel and lightweight generation and distillation scheme to consolidate client
models at the server. We formulate this problem for image classification and
establish strong baselines for comparison, conduct experiments on CIFAR-100 as
well as challenging, large-scale datasets like ImageNet-R and DomainNet. Our
approach outperforms both existing methods and our own baselines by as much as
7% while significantly reducing communication and client-level computation
costs.

本文针对连续联邦学习中广泛存在、但鲜有研究的 “灾难性遗忘” 和数据异质性问题，提出一种基于提示的、轻量级的生成与蒸馏方案实现客户端模型同步，此方案仅需要发送提示和分类器头，不需要存储任何数据，实验结果表明在 CIFAR-100、ImageNet-R、DomainNet 等数据集上同时显著提升了性能并极大地减少了通信和客户端计算成本。

HePCo: 数据无关的异构提示整合用于连续联邦学习

HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual  Federated Learning

Federated Learning (FL) is a learning paradigm that protects privacy by
keeping client data on edge devices. However, optimizing FL in practice can be
difficult due to the diversity and heterogeneity of the learning system.
Despite recent research efforts to improve the optimization of heterogeneous
data, the impact of time-evolving heterogeneous data in real-world scenarios,
such as changing client data or intermittent clients joining or leaving during
training, has not been studied well. In this work, we propose Continual
Federated Learning (CFL), a flexible framework for capturing the time-evolving
heterogeneity of FL. CFL can handle complex and realistic scenarios, which are
difficult to evaluate in previous FL formulations, by extracting information
from past local data sets and approximating local objective functions. We
theoretically demonstrate that CFL methods have a faster convergence rate than
FedAvg in time-evolving scenarios, with the benefit depending on approximation
quality. Through experiments, we show that our numerical findings match the
convergence analysis and that CFL methods significantly outperform other
state-of-the-art FL baselines.

本研究提出 Continual Federated Learning (CFL) 框架来处理时变异构数据，该框架可以从过去的本地数据集中提取信息和逼近本地目标函数，从而比之前的 FL 方法在复杂和现实场景下具有更快的收敛速度。