StateFL: State Channels Powered Federated Learning

Federated Learning (FL) is a decentralized machine learning approach that provides a privacy-friendly way of training models by keeping the datasets of participating parties private. Some challenges FL faces are the lack of incentives to encourage participation in the learning process, as well as preventing potential cyber attacks that tamper...

Analysis on the Vulnerability of Multi-Server Federated Learning Against Model Poisoning Attacks

Abstract— Federated Learning (FL) makes it possible for a network of clients to jointly train a machine learning model, while also keeping the training data private. There are several approaches when designing a FL network and while most existing research is focused on a single-server design, new and promising variations are arising that make...

Backdoor attacks in federated learning with regression

Machine learning, a pivotal aspect of artificial intelligence, has dramatically altered our interaction with technology and our handling of extensive data. Through its ability to learn and make decisions from patterns and previous experiences, machine learning is growing in influence on different aspects of our lives. It is, however, shown that...

Federated Learning with Rebalanced Dataset

With the widespread application of artificial intelligence, centralized machine learning approaches, which require access to users' local data, have raised concerns about data privacy. In response, federated learning, an architecture that aggregates models trained locally with local data, has been proposed. This approach addresses the data...

MAPL: Model Agnostic Peer-to-Peer Learning

Current methods in Federated and Decentralized learning presume that all clients share the same model architecture, assuming model homogeneity. However, in practice, this assumption may not always hold due to hardware differences. While prior research has addressed model heterogeneity in Federated Learning, it remains unexplored in fully...

Multi-server Asynchronous Federated Learning

In federated learning systems, a server maintains a global model trained by a set of clients based on their local datasets. Conventional synchronous FL systems are very sensitive to system heterogeneity since the server needs to wait for the slowest clients in each round. Asynchronous fl partially addresses this bottleneck by dealing with...

Robust backdoor attack against federated learning

Current backdoor attacks against federated learning (FL) strongly rely on universal triggers or semantic patterns, which can be easily detected and filtered by certain defense mechanisms such as norm clipping, comparing parameter divergences among local updates. In this work, we propose a new stealthy and robust backdoor attack with flexible...

Tailoring Attacks To Federated Continual Learning Models

Federated learning enables training machine learning models on decentralized data sources without centrally aggregating sensitive information. Continual learning, on the other hand, focuses on learning and adapting to new tasks over time while avoiding the catastrophic forgetting of knowledge from previously encountered tasks. Federated...

Training diffusion models with federated learning: A communication-efficient model for cross-silo federated image generation

The training of diffusion-based models for image generation is predominantly controlled by a select few Big Tech companies, raising concerns about privacy, copyright, and data authority due to the lack of transparency regarding training data. Hence, we propose a federated diffusion model scheme that enables the independent and collaborative...

Federated learning: a comparison of methods: How do different Federated Learning frameworks compare?

Federated Learning is a machine learning paradigm for decentralized training over different clients. The training happens in rounds where each client learns a specific model which is then aggregated by a central server and passed back to the clients. Since the paradigm’s inception, many frameworks that provide Federated Learning tools and...

Federated learning: A comparison of methods: How do different ML models compare to each other

Federated learning (FL) has emerged as a promis-ing approach for training machine learning models using geographically distributed data. This paper presents a comprehensive comparative study of var-ious machine learning models in the context of FL. The aim is to evaluate the efficacy of these models in different data distribution scenarios and...

End-to-End Federated Diffusion Generative Models for Tabular Data

Tabular data is widely used in various fields and applications, making the synthesis of such data an active area of research. One important aspect of this research is the development of methods for privacy-preserving data synthesis, which aims to generate synthetic data that retains statistical properties while protecting the privacy of...

Client self-defense against model poisoning in federated learning

Federated Learning is highly susceptible to backdoor and targeted attacks as participants can manipulate their data and models locally without any oversight on whether they follow the correct process. There are a number of server-side defenses that mitigate the attacks by modifying or rejecting local updates submitted by clients. However, we...

Approximate Gradient Inversion Attack on Federated Learning

Federated learning is a private-by-design distributed learning paradigm where clients train local models on their own data before a central server aggregates their local updates to compute a global model. Depending on the aggregation method used, the local updates are either the gradients or the weights of local learning models. Unfortunately,...

MULTI-FLGANs: Multi Distributed Adversarial Networks for Non-IID distributed datasets

Federated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled GANs to benefit from the rich distributed training data while preserving privacy However,in a non-iid setting, current federated GAN architectures are unstable, struggling to learn the distinct features and vulnerable to mode...

Using autoencoders on differentially private federated learning GANs

Machine learning has been applied to almost all fields of computer science over the past decades. The introduction of GANs allowed for new possibilities in fields of medical research and text prediction. However, these new fields work with ever more privacy-sensitive data. In order to maintain user privacy, a combination of federated learning,...

Federated learning from non-iid data: Improving accuracy through data-augmentation and communication efficiency

Federated learning allows multiple parties to collaboratively develop a deep learning model, without sharing private data. Models can be generated from the most up-to-date data while taking unique and not publicly available data into account. However, the distributed nature of federated learning causes problems too, and clients are not...

A study on Privacy-Preserving Federated Learning and enhancement through Transfer Learning

Privacy in today's world is a very important topic and all the more important when sizeable amounts of data are needed in Neural Network processing models. Federated Learning is a technique which aims to decentralize the training process in order to allow the clients to maintain their privacy, while also contributing to a broader learning...