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Found 15 papers, 5 papers with code
ATP: Enabling Fast LLM Serving via Attention on Top Principal Keys
In particular, ATP barely loses accuracy with only $1/2$ principal keys, and only incurs around $2\%$ accuracy drops with $1/4$ principal keys.
All Rivers Run to the Sea: Private Learning with Asymmetric Flows
The main part flows into a small model while the residuals are offloaded to a large model.
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls
Third, we compute the complexity of the convex hulls in hyperbolic spaces to assess the extent of data leakage; at the same time, in order to limit communication cost for the hulls, we propose a new quantization method for the Poincar\'e disc coupled with Reed-Solomon-like encoding.
Machine Unlearning of Federated Clusters
We introduce, for the first time, the problem of machine unlearning for FC, and propose an efficient unlearning mechanism for a customized secure FC framework.
Federated Learning of Large Models at the Edge via Principal Sub-Model Training
However, the heterogeneous-client setting requires some clients to train full model, which is not aligned with the resource-constrained setting; while the latter ones break privacy promises in FL when sharing intermediate representations or labels with the server.
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge
A possible solution to this problem is to utilize off-the-shelf sparse learning algorithms at the clients to meet their resource budget.
Basil: A Fast and Byzantine-Resilient Approach for Decentralized Training
As our main contribution, we propose Basil, a fast and computationally efficient Byzantine robust algorithm for decentralized training systems, which leverages a novel sequential, memory assisted and performance-based criteria for training over a logical ring while filtering the Byzantine users.
Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks
For minimizing the epoch deadline time at the MEC server, we provide a tractable approach for finding the amount of coding redundancy and the number of local data points that a client processes during training, by exploiting the statistical properties of compute as well as communication delays.
Secure and Fault Tolerant Decentralized Learning
To implement our novel per-client criteria for fault mitigation, DiverseFL creates a TEE-based secure enclave within the FL server, which in addition to performing secure aggregation for carrying out the global model update step, securely receives a small representative sample of local data from each client only once before training, and computes guiding updates for each participating client during training.
Distributed, Parallel, and Cluster Computing
Coded Computing for Federated Learning at the Edge
Federated Learning (FL) is an exciting new paradigm that enables training a global model from data generated locally at the client nodes, without moving client data to a centralized server.
Half-skyrmions and Bimerons in an antiferromagnetic insulator at room temperature
In the quest for post-CMOS technologies, ferromagnetic skyrmions and their anti-particles have shown great promise as topologically protected solitonic information carriers in memory-in-logic or neuromorphic devices.
Materials Science
Coded Federated Learning
Here, model parameters are computed locally by each client device and exchanged with a central server, which aggregates the local models for a global view, without requiring sharing of training data.
A Pre-defined Sparse Kernel Based Convolution for Deep CNNs
To explore the potential of this approach, we have experimented with two widely accepted datasets, CIFAR-10 and Tiny ImageNet, in sparse variants of both the ResNet18 and VGG16 architectures.
CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning
That is, it parallelizes the communications over a tree topology leading to efficient bandwidth utilization, and carefully designs a redundant data set allocation and coding strategy at the nodes to make the proposed gradient aggregation scheme robust to stragglers.
Coded Computation over Heterogeneous Clusters
There have been recent results that demonstrate the impact of coding for efficient utilization of computation and storage redundancy to alleviate the effect of stragglers and communication bottlenecks in homogeneous clusters.
Distributed, Parallel, and Cluster Computing Information Theory Information Theory
