github.com/fedimint/fedimint
fedimint/fedimint
Federated E-Cash Mint (⭐ 671)
www.bing.com/ck/a?!&&p=8985e71ad64acdec8a32b67658eff9bdec30845185fcb004ea0f0e7ef9cc08f3JmltdHM9MTc3MjU4MjQwMA&ptn=3&ver=2&hsh=4&fclid=38fd60ba-b77e-69d8-0e47-77a8b6c16847&u=a1aHR0cHM6Ly93d3cucmlzcy5uZXQvUklTU0ludGVsLw&ntb=1
RISSIntel - Regional Information Sharing Systems
RISSIntel permits federated searching across many systems without requiring the RISSNET user to have a separate user account for each partner system. Millions of intelligence records are available …
arxiv.org/abs/2404.06641v1
Federated learning model for predicting major postoperative complications
Background: The accurate prediction of postoperative complication risk using Electronic Health Records (EHR) and artificial intelligence shows great potential. Training a robust artificial intelligence model typically requires large-scale and diverse...
arxiv.org/abs/2404.17916v3
FedCoSR: Personalized Federated Learning with Contrastive Shareable Representati…
Heterogeneity arising from label distribution skew and data scarcity can cause inaccuracy and unfairness in intelligent communication applications that heavily rely on distributed computing. To deal with it, this paper proposes a novel personalized f...
arxiv.org/abs/2508.02866v3
PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Wor…
Large Language Models (LLMs) and other foundation models are increasingly used as the core of AI agents. In agentic workflows, these agents plan tasks, interact with humans and peers, and influence scientific outcomes across federated and heterogeneo...
arxiv.org/abs/2410.02443v1
Clinnova Federated Learning Proof of Concept: Key Takeaways from a Cross-border…
Clinnova, a collaborative initiative involving France, Germany, Switzerland, and Luxembourg, is dedicated to unlocking the power of precision medicine through data federation, standardization, and interoperability. This European Greater Region initia...
arxiv.org/abs/2303.08871v1
WIP: Federated Learning for Routing in Swarm Based Distributed Multi-Hop Network…
Unmanned Aerial Vehicles (UAVs) are a rapidly emerging technology offering fast and cost-effective solutions for many areas, including public safety, surveillance, and wireless networks. However, due to the highly dynamic network topology of UAVs, tr...
github.com/Plume-org/Plume
Plume-org/Plume
Federated blogging application, thanks to ActivityPub (now on https://git.joinplu.me/ — this is just a mirror) (⭐ 2204)
en.wikipedia.org/wiki/Head_of_government
Head of government - Wikipedia
In the executive branch, the head of government is the highest or the 2nd-highest official of a sovereign state, a federated state, an autonomous region
arxiv.org/abs/2201.02783v1
A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Di…
In a modern power system, real-time data on power generation/consumption and its relevant features are stored in various distributed parties, including household meters, transformer stations and external organizations. To fully exploit the underlying...
arxiv.org/abs/1912.01684v2
Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collabo…
In this paper, we propose Helios, a heterogeneity-aware FL framework to tackle the straggler issue. Helios identifies individual devices' heterogeneous training capability, and therefore the expected neural network model training volumes regarding th...
arxiv.org/abs/2211.00919v1
Hydra -- A Federated Data Repository over NDN
Today's big data science communities manage their data publication and replication at the application layer. These communities utilize myriad mechanisms to publish, discover, and retrieve datasets - the result is an ecosystem of either centralized, o...
arxiv.org/abs/2303.10677v1
A Survey of Federated Learning for Connected and Automated Vehicles
Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to alleviate the issues of accidents, traffic congestion, and pollutant emissions, leading to a safe, efficient, and sustaina...
arxiv.org/abs/2311.00465v1
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized a…
Decentralized and asynchronous communications are two popular techniques to speedup communication complexity of distributed machine learning, by respectively removing the dependency over a central orchestrator and the need for synchronization. Yet, c...
www.bing.com/ck/a?!&&p=da514bd5d9a81dbe4765571949c97b5c6212c8423489f0fe302d9753a6069e59JmltdHM9MTc3MjQ5NjAwMA&ptn=3&ver=2&hsh=4&fclid=3c58b66d-24be-61d7-29c2-a17c25fd603a&u=a1aHR0cHM6Ly93d3cucmlzcy5uZXQvUklTU0ludGVsLw&ntb=1
RISSIntel - Regional Information Sharing Systems
RISSIntel permits federated searching across many systems without requiring the RISSNET user to have a separate user account for each partner system. Millions of intelligence records are available …
arxiv.org/abs/2105.14659v1
Federated Learning for Industrial Internet of Things in Future Industries
The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries. Recently, artificial intelligence (AI) has been widely utilized for realizing in...
arxiv.org/abs/2305.03584v3
Now It Sounds Like You: Learning Personalized Vocabulary On Device
In recent years, Federated Learning (FL) has shown significant advancements in its ability to perform various natural language processing (NLP) tasks. This work focuses on applying personalized FL for on-device language modeling. Due to limitations o...
arxiv.org/abs/2205.15987v4
VFed-SSD: Towards Practical Vertical Federated Advertising
As an emerging secure learning paradigm in lever-aging cross-agency private data, vertical federatedlearning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advert...
arxiv.org/abs/2209.14086v2
Momentum Gradient Descent Federated Learning with Local Differential Privacy
Nowadays, the development of information technology is growing rapidly. In the big data era, the privacy of personal information has been more pronounced. The major challenge is to find a way to guarantee that sensitive personal information is not di...
arxiv.org/abs/2312.00051v1
MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigatio…
The membership inference attack (MIA) is a popular paradigm for compromising the privacy of a machine learning (ML) model. MIA exploits the natural inclination of ML models to overfit upon the training data. MIAs are trained to distinguish between tr...