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Federated Learning: Opportunities and Challenges
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Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple devices collaboratively learn a machine learning model without sharing their private data under the supervision of a central server. This offers ample opportunities in critical domains such as healthcare, finance etc, where it is risky to share private user information to other organisations or devices. While FL appears to be a promising Machine Learning (ML) technique to keep the local data private, it is also vulnerable to attacks like other ML models. Given the growing interest in the FL domain, this report discusses the opportunities and challenges in federated learning.
Forward citations
Cited by 7 Pith papers
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IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
Privacy-aware bucketing plus parameter-level shuffling disrupts non-IID gradient structure in HDP-FL, cutting recoverability >60% and surrogate accuracy from 0.78 to 0.33 while preserving ε-aware aggregation utility.
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Sarus: Privacy-Preserving Multi-Vendor Perception Fusion via Homomorphic Encryption
Sarus is an HE-based framework that fuses vendors' Gaussian-moment detection summaries in encrypted form, with linear-scaling server fusion and near-identical output to plaintext fusion.
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What Your Model Threw Away and Why You'll Want It Back: Masking, Fingerprinting, and Privacy from Discarded Geometry
For any smooth model on a Lie-group-representation input space, the set of group elements invisible at a given input (the null fiber) has codimension one and can be found by Newton iteration, enabling pointwise maskin...
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Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models
FlexP-SFL fine-tunes foundation models on resource-constrained devices through personalized split learning without parameter aggregation, improving accuracy and cutting wall-clock time and communication versus federat...
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A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning
On synthetic cardiac data, FedCVR — a re-implementation of FedAdam with server-side momentum — is reported to reach F1 0.78 / AUC 0.96 under DP (ε≈13.4), beating stateless and other adaptive baselines, though the pape...
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PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction
A federated, privacy-preserving RDSN framework for encrypted image reconstruction whose local differential privacy mechanism is not actually differentially private because it releases low-frequency DCT coefficients wi...
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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios
On 552 German households from OpenMeter, a conditional Wasserstein GAN and a Bernstein normalizing flow (MABF) generate the most realistic synthetic 15-minute residential power profiles, outperforming diffusion, hidde...
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