Federated learning (FL) enables multiple data owners (a.k.a. FL clients) to
collaboratively train machine learning models without disclosing sensitive
private data. Existing FL research mostly focuses on the monopoly scenario in
which a single FL server selects a subset of FL clients to update their local
models in each round of training. In practice, there can be multiple FL servers
simultaneously trying to select clients from the same pool. In this paper, we
propose a first-of-its-kind Fairness-aware Federated Job Scheduling (FairFedJS)
approach to bridge this gap. Based on Lyapunov optimization, it ensures fair
allocation of high-demand FL client datasets to FL jobs in need of them, by
jointly considering the current demand and the job payment bids, in order to
prevent prolonged waiting. Extensive experiments comparing FairFedJS against
four state-of-the-art approaches on two datasets demonstrate its significant
advantages. It outperforms the best baseline by 31.9% and 1.0% on average in
terms of scheduling fairness and convergence time, respectively, while
achieving comparable test accuracy.

基于 Lyapunov 优化，提出了首个公平感知的联邦作业调度（FairFedJS）方法，确保对高需求的联邦学习客户端数据集进行公平分配，以防止长时间等待。通过在两个数据集上与四种最先进的方法进行广泛实验证明，相较于最佳基准线，其在调度公平性和收敛时间方面分别提高了 31.9％和 1.0％，同时实现了可比较的测试准确性。

多任务联合学习的公平性感知工作调度

Fairness-Aware Job Scheduling for Multi-Job Federated Learning

Federated learning (FL) is an emerging machine learning paradigm in which a
central server coordinates multiple participants (a.k.a. FL clients) to train a
model collaboratively on decentralized data with privacy protection. This
paradigm constrains that all clients have to train models with the same
structures (homogeneous). In practice, FL often faces statistical
heterogeneity, system heterogeneity and model heterogeneity challenges. These
challenging issues inspire the field of Model-Heterogeneous Personalized
Federated Learning (MHPFL) which aims to train a personalized and heterogeneous
local model for each FL client. Existing MHPFL approaches cannot achieve
satisfactory model performance, acceptable computational overhead and efficient
communication simultaneously. To bridge this gap, we propose a novel
computation- and communication-efficient model-heterogeneous personalized
Federated learning framework based on LoRA tuning (FedLoRA). It is designed to
incorporate a homogeneous small adapter for each client's heterogeneous local
model. Both models are trained following the proposed iterative training for
global-local knowledge exchange. The homogeneous small local adapters are sent
to the FL server to be aggregated into a global adapter. In this way, FL
clients can train heterogeneous local models without incurring high computation
and communication costs. We theoretically prove the non-convex convergence rate
of FedLoRA. Extensive experiments on two real-world datasets demonstrate that
FedLoRA outperforms six state-of-the-art baselines, beating the best approach
by 1.35% in terms of test accuracy, 11.81 times computation overhead reduction
and 7.41 times communication cost saving.

基于 LoRA 调优的计算和通信高效的模型异构个性化联邦学习框架（FedLoRA）为每个联邦学习客户端设计了一个同质化小适配器，从而使得客户端可以在不产生高计算和通信成本的情况下训练异构化的本地模型。FedLoRA 在两个真实数据集上的实验表明，它在测试准确性方面比六种最先进的基准方法表现更好，准确性提高了 1.35%，计算开销减少了 11.81 倍，通信成本降低了 7.41 倍。

FedLoRA：具有 LoRA 调整的异构模型个性化联邦学习

FedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA  Tuning

Federated learning (FL) has enabled multiple data owners (a.k.a. FL clients)
to train machine learning models collaboratively without revealing private
data. Since the FL server can only engage a limited number of clients in each
training round, FL client selection has become an important research problem.
Existing approaches generally focus on either enhancing FL model performance or
enhancing the fair treatment of FL clients. The problem of balancing
performance and fairness considerations when selecting FL clients remains open.
To address this problem, we propose the Fairness-aware Federated Client
Selection (FairFedCS) approach. Based on Lyapunov optimization, it dynamically
adjusts FL clients' selection probabilities by jointly considering their
reputations, times of participation in FL tasks and contributions to the
resulting model performance. By not using threshold-based reputation filtering,
it provides FL clients with opportunities to redeem their reputations after a
perceived poor performance, thereby further enhancing fair client treatment.
Extensive experiments based on real-world multimedia datasets show that
FairFedCS achieves 19.6% higher fairness and 0.73% higher test accuracy on
average than the best-performing state-of-the-art approach.

通过 Lyapunov 优化的公平感知联邦用户选择（FairFedCS）方法可以在联邦学习中动态调整联邦用户的选择概率，根据联邦用户的声誉、参与联邦任务的次数和对最终模型性能的贡献，共同考虑性能和公平性。在真实的多媒体数据集上进行的大量实验表明，FairFedCS 平均实现了 19.6% 更高的公平性和 0.73% 更高的测试准确性，超过了最佳性能的现有方法。