Federated fine-tuning (FFT) adapts foundation models to decentralized data but remains fragile under heterogeneous client distributions due to local drift, i.e., client-level update divergences that induce systematic bias and amplified variance in the global model. Existing aggregation and personalization methods largely correct drift post hoc, which proves brittle under extreme non-IID conditions. We introduce OvA-LP, a minimalist framework that is, to our knowledge, the first explicitly designed to suppress drift at its source within the PEFT-based FFT paradigm. OvA-LP combines linear probing on a frozen encoder with a one-vs-all head and a simple two-stage procedure, preserving pretrained feature geometry and decoupling logits to prevent the mechanisms that amplify drift. On CIFAR-100 with 100 clients, averaged over shard-1, shard-2, and Bernoulli-Dirichlet partitions, OvA-LP retains 95.9% of its IID accuracy, whereas state-of-the-art FFT baselines retain only 10.1% (PFPT) and 34.5% (FFT-MoE) under the same conditions. OvA-LP further maintains resilience under both symmetric and asymmetric label noise. In addition, precomputing encoder features makes per-round cost nearly independent of encoder size. Together, these results demonstrate that OvA-LP provides a principled and efficient basis for robust FFT under heterogeneity.

Abstract--The right to be forgotten is a fundamental principle of privacy-preserving regulations and extends to Machine Learning (ML) paradigms such as Federated Learning (FL). While FL enhances privacy by enabling collaborative model training without sharing private data, trained models still retain the influence of training data. Federated Unlearning (FU) methods recently proposed often rely on impractical assumptions for real-world FL deployments, such as storing client update histories or requiring access to a publicly available dataset. T o address these constraints, this paper introduces a novel method that leverages negated Pseudo-gradients Updates for Federated Unlearning (PUF). Our approach only uses standard client model updates, which are employed during regular FL rounds, and interprets them as pseudo-gradients. When a client needs to be forgotten, we apply the negation of their pseudo-gradients, appropriately scaled, to the global model. Unlike state-of-the-art mechanisms, PUF seamlessly integrates with FL workflows, incurs no additional computational and communication overhead beyond standard FL rounds, and supports concurrent unlearning requests. We extensively evaluated the proposed method on two well-known benchmark image classification datasets (CIF AR-10 and CIF AR-100) and a real-world medical imaging dataset for segmentation (ProstateMRI), using three different neural architectures: two residual networks and a vision transformer . The experimental results across various settings demonstrate that PUF achieves state-of-the-art forgetting effectiveness and recovery time, without relying on any additional assumptions. N today's digital landscape, privacy has become a major concern, as reflected by the emergence of robust regulatory frameworks worldwide [1]. The European Union (EU) has consistently emphasized the importance of protecting personal data, exemplified by the introduction of the General Data Protection Regulation (GDPR) in 2016 [2]. Most recently, in May 2024, the EU enacted Regulation 2024/1183 [3], establishing the European Digital Identity Framework that empowers individuals with fine-grained control over their information. One of the key rights of these regulations is the right to be forgotten, which allows individuals to request the deletion of their previously shared data. Similar rights are central to other major privacy laws worldwide, such as the California Consumer Privacy Act (CCP A) [4] where the right to delete grants California residents the on-demand removal of personal data held by businesses. Alessio Mora, Rebecca Montanari, and Paolo Bellavista are with the Department of Computer Science and Engineering, University of Bologna, Bologna, Italy (e-mail: {name.surname}@unibo.it).

Federated fine-tuning (FFT) of large language models (LLMs) has recently emerged as a promising solution to enable domain-specific adaptation while preserving data privacy. Despite its benefits, FFT on resource-constrained clients relies on the high computational and memory demands of full-model fine-tuning, which limits the potential advancement. This paper presents FedSODA, a resource-efficient FFT framework that enables clients to adapt LLMs without accessing or storing the full model. Specifically, we first propose a similarity group pruning (SGP) module, which prunes redundant layers from the full LLM while retaining the most critical layers to preserve the model performance. Moreover, we introduce an orchestrated distillation alignment (ODA) module to reduce gradient divergence between the sub-LLM and the full LLM during FFT. Through the use of the QLoRA, clients only need to deploy quantized sub-LLMs and fine-tune lightweight adapters, significantly reducing local resource requirements. We conduct extensive experiments on three open-source LLMs across a variety of downstream tasks. The experimental results demonstrate that FedSODA reduces communication overhead by an average of 70.6%, decreases storage usage by 75.6%, and improves task accuracy by 3.1%, making it highly suitable for practical FFT applications under resource constraints.

Federated Learning enables collaborative fine-tuning of Large Language Models (LLMs) across decentralized Non-Independent and Identically Distributed (Non-IID) clients, but such models' massive parameter sizes lead to significant memory and communication challenges. This work introduces Meerkat, a sparse zeroth-order optimization (ZO) method designed for federated LLM fine-tuning. By limiting fine-tuning to a transferable, static, extremely sparse subset of parameters, Meerkat achieves remarkable communication efficiency, enabling cost-effective high-frequency synchronization. With theoretical analysis and experiments, we show that this high-frequency communication effectively mitigates Non-IID data challenges and leads to superior performance compared to full-parameter ZO. Furthermore, experiment results show that Meerkat outperforms existing sparsity baselines with better performance at the same communication frequency. To further handle Non-IID drift, Meerkat leverages traceable local updates and forms a virtual path for each client. This virtual path mechanism reveals the GradIP phenomenon: the inner products between LLM pre-training gradients maintained by server and client gradients estimated via ZO converges for extreme Non-IID clients but oscillates for IID ones. This distinct behavior provides a signal for identifying clients with extreme data heterogeneity. Using this signal, Meerkat-vp is proposed to analyze GradIP trajectories to identify extreme Non-IID clients and applies early stopping to enhance aggregated model quality. Experiments confirm that Meerkat and Meerkat-vp significantly improve the efficiency and effectiveness of ZO federated LLM fine-tuning.

Federated Learning (FL) enables collaborative machine learning across decentralized data sources without sharing raw data. It offers a promising approach to privacy-preserving AI. However, FL remains vulnerable to adversarial threats from malicious participants, referred to as Byzantine clients, who can send misleading updates to corrupt the global model. Traditional aggregation methods, such as simple averaging, are not robust to such attacks. More resilient approaches, like the Krum algorithm, require prior knowledge of the number of malicious clients, which is often unavailable in real-world scenarios. To address these limitations, we propose Average-rKrum (ArKrum), a novel aggregation strategy designed to enhance both the resilience and privacy guarantees of FL systems. Building on our previous work (rKrum), ArKrum introduces two key innovations. First, it includes a median-based filtering mechanism that removes extreme outliers before estimating the number of adversarial clients. Second, it applies a multi-update averaging scheme to improve stability and performance, particularly when client data distributions are not identical. We evaluate ArKrum on benchmark image and text datasets under three widely studied Byzantine attack types. Results show that ArKrum consistently achieves high accuracy and stability. It performs as well as or better than other robust aggregation methods. These findings demonstrate that ArKrum is an effective and practical solution for secure FL systems in adversarial environments.

One global model in federated learning (FL) might not be sufficient to serve many clients with non-IID tasks and distributions. While there has been advances in FL to train multiple global models for better personalization, they only provide limited choices to clients so local finetuning is still indispensable. In this paper, we propose a novel ``FedMerge'' approach that can create a personalized model per client by simply merging multiple global models with automatically optimized and customized weights. In FedMerge, a few global models can serve many non-IID clients, even without further local finetuning. We formulate this problem as a joint optimization of global models and the merging weights for each client. Unlike existing FL approaches where the server broadcasts one or multiple global models to all clients, the server only needs to send a customized, merged model to each client. Moreover, instead of periodically interrupting the local training and re-initializing it to a global model, the merged model aligns better with each client's task and data distribution, smoothening the local-global gap between consecutive rounds caused by client drift. We evaluate FedMerge on three different non-IID settings applied to different domains with diverse tasks and data types, in which FedMerge consistently outperforms existing FL approaches, including clustering-based and mixture-of-experts (MoE) based methods.

Federated Learning (FL) has attracted considerable interest due to growing privacy concerns and regulations like the General Data Protection Regulation (GDPR), which stresses the importance of privacy-preserving and fair machine learning approaches. In FL, model training takes place on decentralized data, so as to allow clients to upload a locally trained model and receive a globally aggregated model without exposing sensitive information. However, challenges related to fairness-such as biases, uneven performance among clients, and the "free rider" issue complicates its adoption. In this paper, we examine the use of Mutual Information (MI)-based loss functions to address these concerns. MI has proven to be a powerful method for measuring dependencies between variables and optimizing deep learning models. By leveraging MI to extract essential features and minimize biases, we aim to improve both the fairness and effectiveness of FL systems. Through extensive benchmarking, we assess the impact of MI-based losses in reducing disparities among clients while enhancing the overall performance of FL.

With the booming development of prosumers, there is an urgent need for a prosumer energy management system to take full advantage of the flexibility of prosumers and take into account the interests of other parties. However, building such a system will undoubtedly reveal users' privacy. In this paper, by solving the non-independent and identical distribution of data (Non-IID) problem in federated learning with federated cluster average(FedClusAvg) algorithm, prosumers' information can efficiently participate in the intelligent decision making of the system without revealing privacy. In the proposed FedClusAvg algorithm, each client performs cluster stratified sampling and multiple iterations. Then, the average weight of the parameters of the sub-server is determined according to the degree of deviation of the parameter from the average parameter. Finally, the sub-server multiple local iterations and updates, and then upload to the main server. The advantages of FedClusAvg algorithm are the following two parts. First, the accuracy of the model in the case of Non-IID is improved through the method of clustering and parameter weighted average. Second, local multiple iterations and three-tier framework can effectively reduce communication rounds.