arXiv:2511.16523v1 Announce Type: new 
Abstract: Federated learning (FL) enables clients to collaboratively train a shared model in a distributed manner, setting it apart from traditional deep learning paradigms. However, most existing FL research assumes consistent client participation, overlooking the practical scenario of dynamic participation (DPFL), where clients may intermittently join or leave during training. Moreover, no existing benchmarking framework systematically supports the study of DPFL-specific challenges. In this work, we present the first open-source framework explicitly designed for benchmarking FL models under dynamic client participation. Our framework provides configurable data distributions, participation patterns, and evaluation metrics tailored to DPFL scenarios. Using this platform, we benchmark four major categories of widely adopted FL models and uncover substantial performance degradation under dynamic participation. To address these challenges, we further propose Knowledge-Pool Federated Learning (KPFL), a generic plugin that maintains a shared knowledge pool across both active and idle clients. KPFL leverages dual-age and data-bias weighting, combined with generative knowledge distillation, to mitigate instability and prevent knowledge loss. Extensive experiments demonstrate the significant impact of dynamic participation on FL performance and the effectiveness of KPFL in improving model robustness and generalization.

يتيح التعلم الفيدرالي (FL) للعملاء تدريب نموذج مشترك بشكل تعاوني بطريقة موزعة، مما يميزه عن paradigms التعلم العميق التقليدية. تقدم هذه الدراسة إطار عمل مفتوح المصدر لتقييم نماذج FL تحت مشاركة العملاء الديناميكية (DPFL)، حيث تتناول التحديات المتعلقة بانضمام العملاء أو مغادرتهم بشكل متقطع أثناء التدريب. يوفر الإطار توزيعات بيانات قابلة للتكوين ومقاييس تقييم، مما يكشف عن تدهور كبير في الأداء تحت ظروف DPFL.

El aprendizaje federado (FL) permite a los clientes entrenar colaborativamente un modelo compartido de manera distribuida, diferenciándose de los paradigmas tradicionales de aprendizaje profundo. Esta investigación presenta un nuevo marco de código abierto para evaluar modelos de FL bajo participación dinámica de clientes (DPFL), abordando los desafíos de la participación intermitente de los clientes durante el entrenamiento. El marco ofrece distribuciones de datos configurables y métricas de evaluación, revelando una degradación significativa del rendimiento en modelos de FL bajo condiciones …

L'apprentissage fédéré (FL) permet aux clients de former collaborativement un modèle partagé de manière distribuée, se distinguant de l'apprentissage profond traditionnel. Cette recherche présente un nouveau cadre open-source pour évaluer les modèles FL sous participation dynamique des clients (DPFL), abordant les défis liés à l'entrée et à la sortie intermittentes des clients pendant la formation. Le cadre offre des distributions de données configurables et des métriques d'évaluation, révélant une dégradation significative des performances des modèles FL dans des conditions DPFL.

Federated learning (FL) allows clients to collaboratively train a shared model in a distributed manner, differing from traditional deep learning. This research introduces a new open-source framework for benchmarking FL models under dynamic client participation (DPFL), addressing the challenges of clients intermittently joining or leaving during training. The framework offers configurable data distributions and evaluation metrics, revealing significant performance degradation in FL models under DPFL conditions.

Dynamic Participation in Federated Learning: Benchmarks and a Knowledge Pool Plugin

arXiv:2601.08216v1 Announce Type: new 
Abstract: Federated learning protocols require repeated synchronization between clients and a central server, with convergence rates depending on learning rates, data heterogeneity, and client sampling. This paper asks whether iterative communication is necessary for distributed linear regression. We show it is not. We formulate federated ridge regression as a distributed equilibrium problem where each client computes local sufficient statistics -- the Gram matrix and moment vector -- and transmits them once. The server reconstructs the global solution through a single matrix inversion. We prove exact recovery: under a coverage condition on client feature matrices, one-shot aggregation yields the centralized ridge solution, not an approximation. For heterogeneous distributions violating coverage, we derive non-asymptotic error bounds depending on spectral properties of the aggregated Gram matrix. Communication reduces from $\mathcal{O}(Rd)$ in iterative methods to $\mathcal{O}(d^2)$ total; for high-dimensional settings, we propose and experimentally validate random projection techniques reducing this to $\mathcal{O}(m^2)$ where $m \ll d$. We establish differential privacy guarantees where noise is injected once per client, eliminating the composition penalty that degrades privacy in multi-round protocols. We further address practical considerations including client dropout robustness, federated cross-validation for hyperparameter selection, and comparison with gradient-based alternatives. Comprehensive experiments on synthetic heterogeneous regression demonstrate that one-shot fusion matches FedAvg accuracy while requiring up to $38\times$ less communication. The framework applies to kernel methods and random feature models but not to general nonlinear architectures.

أظهرت دراسة حديثة نهجًا مبتكرًا في الانحدار المتحد الفيدرالي، حيث أثبتت أن التواصل التكراري بين العملاء والخادم المركزي ليس ضروريًا لتحقيق الاسترداد الدقيق للحل المركزي. من خلال تجميع الإحصائيات الكافية من العملاء في إرسال واحد، يمكن للخادم إعادة بناء الحل العالمي من خلال عكس المصفوفة، مما يقلل بشكل كبير من عبء التواصل.

Un estudio reciente presenta un enfoque novedoso para la regresión ridge federada, demostrando que la comunicación iterativa entre los clientes y un servidor central no es necesaria para lograr la recuperación exacta de la solución centralizada. Al agregar estadísticas suficientes de los clientes en una sola transmisión, el servidor puede reconstruir la solución global mediante la inversión de matrices, reduciendo significativamente la sobrecarga de comunicación.

Une étude récente présente une approche novatrice de la régression ridge fédérée, démontrant que la communication itérative entre les clients et un serveur central n'est pas nécessaire pour obtenir une récupération exacte de la solution centralisée. En agrégeant les statistiques suffisantes des clients en une seule transmission, le serveur peut reconstruire la solution globale par inversion de matrice, réduisant ainsi considérablement la surcharge de communication.

A recent study introduces a novel approach to federated ridge regression, demonstrating that iterative communication between clients and a central server is unnecessary for achieving exact recovery of the centralized solution. By aggregating sufficient statistics from clients in a single transmission, the server can reconstruct the global solution through matrix inversion, significantly reducing communication overhead.

One-Shot Federated Ridge Regression: Exact Recovery via Sufficient Statistic Aggregation

arXiv:2311.12715v3 Announce Type: replace 
Abstract: Federated Learning is an important emerging distributed training paradigm that keeps data private on clients. It is now well understood that by controlling only a small subset of FL clients, it is possible to introduce a backdoor to a federated learning model, in the presence of certain attributes. In this paper, we present a new type of attack that compromises the fairness of the trained model. Fairness is understood to be the attribute-level performance distribution of a trained model. It is particularly salient in domains where, for example, skewed accuracy discrimination between subpopulations could have disastrous consequences. We find that by employing a threat model similar to that of a backdoor attack, an attacker is able to influence the aggregated model to have an unfair performance distribution between any given set of attributes. Furthermore, we find that this attack is possible by controlling only a single client. While combating naturally induced unfairness in FL has previously been discussed in depth, its artificially induced kind has been neglected. We show that defending against attacks on fairness should be a critical consideration in any situation where unfairness in a trained model could benefit a user who participated in its training.

تسلط الأبحاث الحديثة الضوء على نوع جديد من الهجمات على التعلم الفيدرالي (FL) الذي يهدد العدالة في النماذج المدربة، حيث يكشف أن السيطرة على عميل واحد فقط يمكن أن تؤثر على توزيع الأداء عبر سمات مختلفة. يثير هذا مخاوف بشأن نزاهة النماذج في التطبيقات الحساسة حيث تكون العدالة أمرًا حاسمًا.

Investigaciones recientes destacan un nuevo tipo de ataque en el Aprendizaje Federado (FL) que compromete la equidad de los modelos entrenados, revelando que controlar solo un cliente puede sesgar las distribuciones de rendimiento entre diversos atributos. Esto genera preocupaciones sobre la integridad de los modelos en aplicaciones sensibles donde la equidad es crítica.

Des recherches récentes mettent en lumière un nouveau type d'attaque sur l'apprentissage fédéré (FL) qui compromet l'équité des modèles entraînés, révélant que le contrôle d'un seul client peut fausser les distributions de performance entre divers attributs. Cela soulève des inquiétudes quant à l'intégrité des modèles dans des applications sensibles où l'équité est cruciale.

Recent research highlights a new type of attack on Federated Learning (FL) that compromises the fairness of trained models, revealing that controlling just one client can skew performance distributions across various attributes. This raises concerns about the integrity of models in sensitive applications where fairness is critical.

Dynamic Participation in Federated Learning: Benchmarks and a Knowledge Pool Plugin

Was this article worth reading? Share it

LucidQuery AI

Dyad

FastML

AINF Membership

flyline

PrettyPolly

Ready to build your own newsroom?