Related papers: Federated Learning with Dynamic Client Arrival and Departure: Convergence and Rapid Adaptation via Initial Model Construction

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\ extit{FedABC}: Attention-Based Client Selection for Federated Learning with Long-Term View [5.072601407613152]
Federated Learning (FL) allows decentralized clients to collaboratively train an AI model without directly sharing their data, preserving privacy.<n>Clients train local models on private data and share model updates, which a central server aggregates to refine the global model and redistribute it for the next iteration.<n>We propose textitFedABC, an innovative client selection algorithm designed to take a long-term view in managing data heterogeneity and optimizing client participation.
arXiv Detail & Related papers (2025-07-28T14:22:43Z)
Sociodynamics-inspired Adaptive Coalition and Client Selection in Federated Learning [39.58317527488534]
We introduce shortname (Federated Coalition Variance Reduction with Boltzmann Exploration), a variance-reducing algorithm inspired by opinion dynamics over temporal social networks.<n>Our experiments show that in heterogeneous scenarios our algorithm outperforms existing FL algorithms, yielding more accurate results and faster convergence.
arXiv Detail & Related papers (2025-06-03T14:04:31Z)
Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual Learning [49.508844889242425]
We propose a novel server-side FCL pattern in medical domain, Dynamic Allocation Hypernetwork with adaptive model recalibration (FedDAH) FedDAH is designed to facilitate collaborative learning under the distinct and dynamic task streams across clients. For the biased optimization, we introduce a novel adaptive model recalibration (AMR) to incorporate the candidate changes of historical models into current server updates.
arXiv Detail & Related papers (2025-03-25T00:17:47Z)
Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for FCL [49.508844889242425]
We propose a novel server-side FCL pattern in medical domain, Dynamic Allocation Hypernetwork with adaptive model recalibration (textbfFedDAH) For the biased optimization, we introduce a novel adaptive model recalibration (AMR) to incorporate the candidate changes of historical models into current server updates. Experiments on the AMOS dataset demonstrate the superiority of our FedDAH to other FCL methods on sites with different task streams.
arXiv Detail & Related papers (2025-03-23T13:12:56Z)
Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology [31.571298699019255]
Hierarchical Federated Learning (HFL) organizes clients into multiple clusters and utilizes edge nodes for intermediate model aggregations. Current research on HFL mainly focus on enhancing model accuracy, latency, and energy consumption in scenarios with a stable/fixed set of clients. This study delves into optimizing client selection and client-to-edge associations in HFL under intermittent client participation.
arXiv Detail & Related papers (2025-02-13T13:16:10Z)
Feasible Learning [78.6167929413604]
We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bounds the loss for each training sample. Our empirical analysis, spanning image classification, age regression, and preference optimization in large language models, demonstrates that models trained via FL can learn from data while displaying improved tail behavior compared to ERM, with only a marginal impact on average performance.
arXiv Detail & Related papers (2025-01-24T20:39:38Z)
Client-Centric Federated Adaptive Optimization [78.30827455292827]
Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private. We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
arXiv Detail & Related papers (2025-01-17T04:00:50Z)
Incentive-Compatible Federated Learning with Stackelberg Game Modeling [11.863770989724959]
We introduce FLamma, a novel Federated Learning framework based on adaptive gamma-based Stackelberg game. Our approach allows the server to act as the leader, dynamically adjusting a decay factor while clients, acting as followers, optimally select their number of local epochs to maximize their utility. Over time, the server incrementally balances client influence, initially rewarding higher-contributing clients and gradually leveling their impact, driving the system toward a Stackelberg Equilibrium.
arXiv Detail & Related papers (2025-01-05T21:04:41Z)
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning [12.307490659840845]
Federated Learning (FL) combines locally optimized models from various clients into a unified global model.<n>FL encounters significant challenges such as performance degradation, slower convergence, and reduced robustness of the global model.<n>We introduce an innovative dual-strategy approach designed to effectively resolve these issues.
arXiv Detail & Related papers (2024-12-05T18:42:29Z)
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning [57.38427653043984]
Federated learning (FL) has emerged as a prominent approach for collaborative training of machine learning models across distributed clients. We introduce FedCAda, an innovative federated client adaptive algorithm designed to tackle this challenge. We demonstrate that FedCAda outperforms the state-of-the-art methods in terms of adaptability, convergence, stability, and overall performance.
arXiv Detail & Related papers (2024-05-20T06:12:33Z)
An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
arXiv Detail & Related papers (2024-04-29T05:55:23Z)
PraFFL: A Preference-Aware Scheme in Fair Federated Learning [5.9403570178003395]
We propose a Preference-aware scheme in Fair Federated Learning paradigm (called PraFFL) to generate preference-wise model in real time. We theoretically prove that PraFFL can offer the optimal model tailored to an arbitrary preference of each client, and show its linear convergence.
arXiv Detail & Related papers (2024-04-13T11:40:05Z)
FLASH: Federated Learning Across Simultaneous Heterogeneities [54.80435317208111]
FLASH(Federated Learning Across Simultaneous Heterogeneities) is a lightweight and flexible client selection algorithm. It outperforms state-of-the-art FL frameworks under extensive sources of Heterogeneities. It achieves substantial and consistent improvements over state-of-the-art baselines.
arXiv Detail & Related papers (2024-02-13T20:04:39Z)
Federated Learning with Projected Trajectory Regularization [65.6266768678291]
Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
arXiv Detail & Related papers (2023-12-22T02:12:08Z)
Addressing Membership Inference Attack in Federated Learning with Model Compression [8.842172558292027]
Federated Learning (FL) has been proposed as a privacy-preserving solution for machine learning. Recent works have reported that FL can leak private client data through membership inference attacks. We show that effectiveness of these attacks negatively correlates with the size of the client's datasets and model complexity.
arXiv Detail & Related papers (2023-11-29T15:54:15Z)
Utilizing Free Clients in Federated Learning for Focused Model Enhancement [9.370655190768163]
Federated Learning (FL) is a distributed machine learning approach to learn models on decentralized heterogeneous data. We present FedALIGN (Federated Adaptive Learning with Inclusion of Global Needs) to address this challenge.
arXiv Detail & Related papers (2023-10-06T18:23:40Z)
FedJETs: Efficient Just-In-Time Personalization with Federated Mixture of Experts [48.78037006856208]
FedJETs is a novel solution by using a Mixture-of-Experts (MoE) framework within a Federated Learning (FL) setup. Our method leverages the diversity of the clients to train specialized experts on different subsets of classes, and a gating function to route the input to the most relevant expert(s) Our approach can improve accuracy up to 18% in state of the art FL settings, while maintaining competitive zero-shot performance.
arXiv Detail & Related papers (2023-06-14T15:47:52Z)
Confidence-aware Personalized Federated Learning via Variational Expectation Maximization [34.354154518009956]
We present a novel framework for personalized Federated Learning (PFL) PFL is a distributed learning scheme to train a shared model across clients. We present a novel framework for PFL based on hierarchical modeling and variational inference.
arXiv Detail & Related papers (2023-05-21T20:12:27Z)
Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
arXiv Detail & Related papers (2023-05-01T20:04:46Z)
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning [28.185096784982544]
Federated learning (FL) allows multiple clients cooperatively train models without disclosing local data. We propose a heterogeneous-aware FL framework, called FedCG, with adaptive client selection and gradient compression. Experiments on both real-world prototypes and simulations show that FedCG can provide up to 5.3$times$ speedup compared to other methods.
arXiv Detail & Related papers (2022-12-19T14:19:07Z)
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
arXiv Detail & Related papers (2022-04-16T08:08:29Z)
Personalized Federated Learning with First Order Model Optimization [76.81546598985159]
We propose an alternative to federated learning, where each client federates with other relevant clients to obtain a stronger model per client-specific objectives. We do not assume knowledge of underlying data distributions or client similarities, and allow each client to optimize for arbitrary target distributions of interest. Our method outperforms existing alternatives, while also enabling new features for personalized FL such as transfer outside of local data distributions.
arXiv Detail & Related papers (2020-12-15T19:30:29Z)
Faster Non-Convex Federated Learning via Global and Local Momentum [57.52663209739171]
textttFedGLOMO is the first (first-order) FLtexttFedGLOMO algorithm. Our algorithm is provably optimal even with communication between the clients and the server.
arXiv Detail & Related papers (2020-12-07T21:05:31Z)

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