Search Results for author:
Found 10 papers, 1 papers with code
Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning
We propose a novel method, Federated Orthogonal Training (FOT), to overcome these drawbacks and address the global catastrophic forgetting in CFL.
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
In the absence of centralized data, Federated Learning (FL) can benefit from distributed and private data of the FL edge clients for fine-tuning.
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
We show that this resource overhead is caused by an incorrect perspective in all prior work that treats an attack on an aggregate update in the same way as an individual update with a larger batch size.
LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation
When both FedAVG and secure aggregation are used, there is no current method that is able to attack multiple clients concurrently in a federated learning setting.
Federated Analytics: A survey
Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e. g., mobile devices) or silo-ed institutional entities (e. g., hospitals, banks) without sharing the data among parties.
How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?
While SA ensures no additional information is leaked about the individual model update beyond the aggregated model update, there are no formal guarantees on how much privacy FL with SA can actually offer; as information about the individual dataset can still potentially leak through the aggregated model computed at the server.
FairFed: Enabling Group Fairness in Federated Learning
Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as healthcare and recruitment.
A Reinforcement Learning Approach for Scheduling in mmWave Networks
We consider a source that wishes to communicate with a destination at a desired rate, over a mmWave network where links are subject to blockage and nodes to failure (e. g., in a hostile military environment).
Quantizing data for distributed learning
In this paper, we propose an alternate approach to learn from distributed data that quantizes data instead of gradients, and can support learning over applications where the size of gradient updates is prohibitive.
On Distributed Quantization for Classification
We consider the problem of distributed feature quantization, where the goal is to enable a pretrained classifier at a central node to carry out its classification on features that are gathered from distributed nodes through communication constrained channels.
