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Found 24 papers, 6 papers with code
Rethinking Machine Unlearning for Large Language Models
We explore machine unlearning (MU) in the domain of large language models (LLMs), referred to as LLM unlearning.
Enhancing In-context Learning via Linear Probe Calibration
However, applying ICL in real cases does not scale with the number of samples, and lacks robustness to different prompt templates and demonstration permutations.
FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs
We evaluate the performance-fairness trade-off for SISA, and empirically demsontrate that SISA can indeed reduce fairness in LLMs.
Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks
Growing applications of large language models (LLMs) trained by a third party raise serious concerns on the security vulnerability of LLMs. It has been demonstrated that malicious actors can covertly exploit these vulnerabilities in LLMs through poisoning attacks aimed at generating undesirable outputs.
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection
The effective detection of evidence of financial anomalies requires collaboration among multiple entities who own a diverse set of data, such as a payment network system (PNS) and its partner banks.
Single-shot General Hyper-parameter Optimization for Federated Learning
We address the problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
As part of the training, the parties wish to remove unimportant features in the system to improve generalization, efficiency, and explainability.
Federated XGBoost on Sample-Wise Non-IID Data
Federated Learning (FL) is a paradigm for jointly training machine learning algorithms in a decentralized manner which allows for parties to communicate with an aggregator to create and train a model, without exposing the underlying raw data distribution of the local parties involved in the training process.
Federated Unlearning: How to Efficiently Erase a Client in FL?
With privacy legislation empowering the users with the right to be forgotten, it has become essential to make a model amenable for forgetting some of its training data.
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach
Federated Learning (FL) is a novel paradigm for the shared training of models based on decentralized and private data.
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
Privacy-Preserving Machine Learning: Methods, Challenges and Directions
In particular, existing PPML research cross-cut ML, systems and applications design, as well as security and privacy areas; hence, there is a critical need to understand state-of-the-art research, related challenges and a research roadmap for future research in PPML area.
LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning
This global model can be corrupted when Byzantine workers send malicious gradients, which necessitates robust methods for aggregating gradients that mitigate the adverse effects of Byzantine inputs.
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
We empirically demonstrate the applicability for multiple types of ML models and show a reduction of 10%-70% of training time and 80% to 90% in data transfer with respect to the state-of-the-art approaches.
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks.
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning
This approach makes the use of gradient boosted trees practical in enterprise federated learning.
Mitigating Bias in Federated Learning
As methods to create discrimination-aware models develop, they focus on centralized ML, leaving federated learning (FL) unexplored.
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume.
TiFL: A Tier-based Federated Learning System
To this end, we propose TiFL, a Tier-based Federated Learning System, which divides clients into tiers based on their training performance and selects clients from the same tier in each training round to mitigate the straggler problem caused by heterogeneity in resource and data quantity.
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
Participants in a federated learning process cooperatively train a model by exchanging model parameters instead of the actual training data, which they might want to keep private.
A Hybrid Approach to Privacy-Preserving Federated Learning
Federated learning facilitates the collaborative training of models without the sharing of raw data.
Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering
While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern.
Adversarial Robustness Toolbox v1.0.0
Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.
