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Found 10 papers, 4 papers with code
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains
In this paper, we study the question of when and how a server could design a FL protocol provably beneficial for all participants.
Human-Guided Fair Classification for Natural Language Processing
While existing research has started to address this gap, current methods are based on hardcoded word replacements, resulting in specifications with limited expressivity or ones that fail to fully align with human intuition (e. g., in cases of asymmetric counterfactuals).
Data Leakage in Federated Averaging
On the popular FEMNIST dataset, we demonstrate that on average we successfully recover >45% of the client's images from realistic FedAvg updates computed on 10 local epochs of 10 batches each with 5 images, compared to only <10% using the baseline.
FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data
In this work we address the problem of fair learning from unreliable training data in the robust multisource setting, where the available training data comes from multiple sources, a fraction of which might not be representative of the true data distribution.
Fairness-Aware PAC Learning from Corrupted Data
Addressing fairness concerns about machine learning models is a crucial step towards their long-term adoption in real-world automated systems.
Fairness Through Regularization for Learning to Rank
Given the abundance of applications of ranking in recent years, addressing fairness concerns around automated ranking systems becomes necessary for increasing the trust among end-users.
On the Sample Complexity of Adversarial Multi-Source PAC Learning
We study the problem of learning from multiple untrusted data sources, a scenario of increasing practical relevance given the recent emergence of crowdsourcing and collaborative learning paradigms.
Robust Learning from Untrusted Sources
Modern machine learning methods often require more data for training than a single expert can provide.
The Convergence of Sparsified Gradient Methods
Distributed training of massive machine learning models, in particular deep neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace.
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory
Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine learning, representing the optimization backbone for training several classic models, from regression to neural networks.
